PSYC 3430 Central Texas University Psychology Summaries Essay

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Hello, need 3 summaries completed regarding reading an article for each brief summary.

PSYC 3430 Research Experience Requirement
Approved Reading List for Research Article Summaries
Ackerman, R., & Goldsmith, M. (2011). Metacognitive regulation of text learning: On screen versus on
paper. Journal of Experimental Psychology: Applied, 17, 18-32. doi: 10.1037/a0022086
Arden, R., Harlaar, N., & Plomin, R. (2007). Sex differences in childhood associations between DNA
markers and general cognitive ability. Journal of Individual Differences, 28(3), 161-164. doi:
10.1027/1614-0001.28.3.161
Anderson, C. A., Benjamin, A. J., & Bartholow, B. D. (1998). Does the gun pull the trigger? Automatic
priming effects of weapon pictures and weapon names. Psychological Science, 9(4), 308-314.
Belisle, J., & Bodur, H. O. (2010). Avatars as information: Perception of consumers based on the avatars
in virtual worlds. Psychology & Marketing, 27, 741-765.
Broberg, A. G., Wessels, H., Lamb, M. E., & Hwang, C. P. (1997). Effects of day care on the development
of cognitive abilities in 8-year-olds: A longitudinal study. Developmental Psychology, 33, 62-69.
Elbel, B., Gyamfi, J., & Kersh, R. (2011). Child and adolescent fast-food choice and the influence of calorie
labeling: A natural experiment. International Journal of Obesity, 35, 493-500. doi:
10.1038/ijo.2011.4
Kayser, D. N., Elliot, A. J., & Feltman, R. (2010). Red and romantic behavior in men viewing women.
European Journal of Social Psychology, 40, 901-908. doi: 10.1002/ejsp.757
Gillen-O’Neel, C., Huynh, V. W., & Fuligni, A. J. (2013). To study or to sleep? The academic costs of extra
studying at the expense of sleep. Child Development, 84(1), 133-142.
Gueguen, N., Jacob, C., & Lamy, L. (2010). “Love is in the air”: Effects of songs with romantic lyrics on
compliance with a courtship request. Psychology of Music, 38, 303-307.
Jones, B. T., Jones, B. C., Thomas, A. P., & Piper, J. (2003). Alcohol consumption increases attractiveness
ratings of opposite-sex faces: A possible third route to risky sex. Addiction, 98, 1069-1075.
Judge, T. A., & Cable, D. M. (2011). When it comes to pay, do the thin win? The effect of weight on pay
for men and women. Journal of Applied Psychology, 96(1), 95-112.
Junco, R. (2015). Student class standing, Facebook use, and academic performance. Journal of Applied
Developmental Psychology, 36, 18-29.
Kasparek, D. G., Corwin, S. J., Valois, R. F., Sargent, R. G., & Morris, R. L. (2008). Selected health
behaviors that influence college freshman weight change. Journal of American College Health,
56(4), 437-444.
Kuo, M. Adlaf, E. M., Lee, H., Gliksman, L., Demers, A., & Wechsler, H. (2002). More Canadian students
drink but American students drink more: Comparing college alcohol use in two countries.
Addiction, 97, 1583-1592.
McGee, E., & Shevlin, M. (2009). Effect of humor on interpersonal attraction and mate selection. Journal
of Psychology, 143(1), 67-77.
Nunes, J. C., & Dreze, X. (2006). The endowed progress effect: How artificial advancement increases
effort. Journal of Consumer Research, 32, 504-512.
Polman, H., de Castro, B. O., van Aken, M. A. G. (2008). Experimental study of the differential effects of
playing versus watching violent video games on children’s aggressive behavior. Aggressive
Behavior, 34, 256-264.
Weinstein, Y., McDermott, K. B., & Roediger, H. L. (2010). A comparison of study strategies for passages:
Rereading, answering questions, and generating questions. Journal of Experimental Psychology:
Applied, 16(3), 308-301.
Zhong, C., Bohns, V. K., & Gino, F. (2010). Good lamps are the best police: Darkness increases dishonesty
and self-interested behavior. Psychological Science, 21, 311-314.
Zhou, X., Vohs, K. D., & Baumeister, R. F. (2009). The symbolic power of money: Reminders of money
after social distress and physical pain. Psychological Science, 20, 700-706.
Journal of Experimental Psychology: Applied
2011, Vol. 17, No. 1, 18 –32
© 2011 American Psychological Association
1076-898X/11/$12.00 DOI: 10.1037/a0022086
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Metacognitive Regulation of Text Learning: On Screen Versus on Paper
Rakefet Ackerman
Morris Goldsmith
Technion–Israel Institute of Technology
University of Haifa
Despite immense technological advances, learners still prefer studying text from printed hardcopy rather
than from computer screens. Subjective and objective differences between on-screen and on-paper
learning were examined in terms of a set of cognitive and metacognitive components, comprising a
Metacognitive Learning Regulation Profile (MLRP) for each study media. Participants studied expository texts of 1000 –1200 words in one of the two media and for each text they provided metacognitive
prediction-of-performance judgments with respect to a subsequent multiple-choice test. Under fixed
study time (Experiment 1), test performance did not differ between the two media, but when study time
was self-regulated (Experiment 2) worse performance was observed on screen than on paper. The results
suggest that the primary differences between the two study media are not cognitive but rather metacognitive—less accurate prediction of performance and more erratic study-time regulation on screen than on
paper. More generally, this study highlights the contribution of metacognitive regulatory processes to
learning and demonstrates the potential of the MLRP methodology for revealing the source of subjective
and objective differences in study performance among study conditions.
Keywords: metacognition, metacomprehension, monitoring and control, text learning, self-regulated
learning, computer-based learning
such as technical characteristics of displays, annotation while
reading, navigation ease, and spatial layout on reader preferences and performance (e.g., Dillon, Richardson, & McKnight,
1990; O’Hara & Sellen, 1997; Richardson, Dillon, &
McKnight, 1989; see Dillon, 1992). Of course, technology has
improved dramatically in the 20 years or so since those results
were obtained. Hence, one might question whether such findings are still relevant. Recent findings, however, indicate a
dislike of on-screen reading even among young adults studying
with current state-of-the-art displays (e.g., Annand, 2008;
Eshet-Alkalai & Geri, 2007; Rogers, 2006; Shaikh, 2004;
Spencer, 2006). From the point of view of hardware engineers
and software designers, this persistent reluctance to read “serious” texts on screen indicates that the large effort invested in
improving reading from computer screens (e.g., Dillon, 1994;
Muter, 1996) has not yet achieved its goals (Dillon, 2002;
Garland & Noyes, 2004; Rogers, 2006; Sellen & Harper, 2002).
The general finding of both objective and subjective difficulties related to on-screen learning makes this an ideal topic
for examination from a metacognitive perspective. A great deal
of research on metacognition and learning has revealed the
crucial role that subjective experience plays in guiding and
regulating the learning process: in the choice of study strategy,
in the allocation and prioritization of study time, in deciding
when one has sufficiently mastered the material, and so forth
(Baker, 1985; Bielaczyc, Pirolli, & Brown, 1995; Bjork, 1994;
Brown, Smiley, & Lawton, 1978; Hacker, 1998; Schunk &
Zimmerman, 1994; Son, 2007). Adopting such a perspective,
the present study examined whether subjective differences between on-screen learning (OSL) and on-paper learning (OPL)
might in fact underlie, rather than merely reflect, objective
differences in learning performance.
Adult readers of today have been using computers extensively for many years. Nevertheless, when one needs to study a
text thoroughly, there is still a strong preference to print out
digital text rather than study it directly from the computer
screen (Buzzetto-More, Sweat-Guy, & Elobaid, 2007; Dilevko
& Gottlieb, 2002; Spencer, 2006). One might assume that this
reluctance is a matter of experience. However, even highly
experienced computer users still prefer print, as Buxton (2008),
principal researcher at Microsoft, admits: “I can’t stand reading
stuff on my computer” (p. 8).
Objective and subjective learning differences between paper
and screen learning have been examined and discussed for some
time. Dillon, McKnight, and Richardson (1988), for example,
pointed to differences by which reading texts on screen is
slower, less accurate, more fatiguing, accompanied by reduced
comprehension, and subjectively less effective than reading
from paper. Additional studies examined the effects of factors
Rakefet Ackerman, Faculty of Industrial Engineering and Management,
Technion-Israel Institute of Technology; Morris Goldsmith, Department of
Psychology, University of Haifa.
We thank Yoram Eshet-Alkalai for valuable discussions relating to the
study. This research was supported by a grant from The Inter-University
Center for E-Learning (IUCEL-MEITAL). Facilities for conducting the
research were provided by the Institute of Information Processing and
Decision Making, University of Haifa, and by the Max Wertheimer Minerva Center for Cognitive Processes and Human Performance. A subset of
the present data, based on fewer participants and including only a subset of
the present MLRP analyses, was previously reported in conference proceedings (Ackerman & Goldsmith, 2008b).
Correspondence concerning this article should be addressed to Rakefet
Ackerman, Faculty of Industrial Engineering and Management, Technion,
Technion City, Haifa 32000, Israel. E-mail: ackerman@ie.technion.ac.il
18
METACOGNITIVE REGULATION OF TEXT LEARNING
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Assessing Metacognitive Regulation of Text Learning
In general, metacognitive theories of learning address the interplay between objective and subjective aspects of the learning
process. Building on concepts and measures from leading metacognitive theories of the control of study time, in the present study
objective (cognitive) and subjective (metacognitive) aspects of
expository text learning were assessed and compared using a new
Metacognitive Learning Regulation Profile (MLRP) methodology,
which provides a multicomponential assessment of learning processes under specific conditions. The comparison between the
MLRPs of OSL and OPL allowed heretofore hidden differences in
underlying components of the learning process between the two
media to be revealed. Table 1 presents the MLRP components that
were examined. These will now be explained.
According to the highly influential discrepancy reduction model
(Butler & Winne, 1995; Dunlosky & Thiede, 1998; Nelson &
Narens, 1990; for a graphic depiction, see Winne & Hadwin,
1998), people begin their study activity by setting a target level of
learning (Le Ny, Denhiere, & Le Taillanter, 1972). Allocation of
study time is then guided by an ongoing subjective assessment
of knowledge level and comparison to the preset target level: when
the subjective knowledge level is satisfactory, that is, when the
target level is reached for a particular item, the learner terminates
the study of that item and moves on to another. Hypothesized study
curves based on the model are depicted in Figure 1 and will be
explained shortly.
Basic measures of study regulation that stem from the discrepancy reduction model are as follows: Prediction of performance
(POP; Maki, 1998b), study time, and test performance (components 2, 7, and 8 in Table 1). POP reflects learners’ ongoing
monitoring and final subjective assessment of their level of knowledge, tapped by having them predict their future test performance
after studying each text (Maki & Serra, 1992; Rawson, Dunlosky,
& McDonald, 2002).1 Study time is an objective measure that is
assumed to reflect the metacognitive control decision to continue
or to terminate study, based on the ongoing monitoring of knowledge level. Test performance is, of course, the ultimate objective
measure of the learner’s success. The relationships among these
three measures produce additional measures that are of interest.
Encoding efficiency can be examined in terms of the amount of
information stored (and retained) into memory during a fixed
amount of study time, that is, when learners have no control over
study time (component 1 in Table 1). This measure yields information about the “objective” efficiency of learning, controlling for
possible differences in the effectiveness of the subjective knowledge monitoring and study-time control decisions that contribute
to self-regulated study.
The MLRP components that reflect the accuracy of the metacognitive monitoring are calibration bias and resolution (components 3 and 4 in Table 1). Calibration bias, or absolute monitoring
accuracy, is calculated as the mean signed deviation between POP
and test score (e.g., Metcalfe, 1998). Based on the discrepancy
reduction model, study should be terminated when POP reaches
the target level of mastery (see Figure 1, stopping points A and B).
Hence, underconfidence, an overly low subjective assessment of
knowledge, will lengthen the study time unnecessarily, wasting
time that could perhaps be invested more effectively in other
materials. By contrast, overconfidence, which is the more common
19
situation (Metcalfe, 1998), will lead to premature study termination and a lower than desired level of performance (e.g., Glenberg,
Wilkinson, & Epstein, 1982; Pressley & Ghatala, 1988). See
Figure 1, stopping point A.
Resolution, or relative monitoring accuracy, indexes the extent
to which POP discriminates between learned and unlearned information (Glenberg, Sanocki, Epstein, & Morris, 1987; Lundeberg,
Fox, & Punćochaŕ, 1994; Maki & Serra, 1992; Rawson, Dunlosky,
& Thiede, 2000; Thiede, Anderson, & Therriault, 2003). Resolution is maximized when all of the better-learned texts are assigned
a higher predicted performance than all of the lesser-learned texts.
This discrimination can then be used by learners to select the most
appropriate material for extra study (Metcalfe & Finn, 2008;
Thiede & Dunlosky, 1999). Relatively low levels of monitoring
resolution have been found in text learning (see Maki, 1998b for a
review), but resolution was improved by some techniques such as
by using immediate rather than delayed prediction (Maki, 1998a)
and by asking for predictions that relate to performance across
multiple test questions (Weaver, 1990).
Moving on to measures of metacognitive control, the discrepancy reduction model entails two factors that relate to the efficiency of control over study time: control criterion and control
sensitivity (components 5 and 6 in Table 1). Control criterion
(“norm of study”; Le Ny et al., 1972) refers to the target level of
knowledge implicitly set by the learner to guide his or her studytime control decisions. According to the discrepancy reduction
model, the higher the criterion that is set for a particular text, the
better that text will be learned (cf. Nelson & Leonesio, 1988), but
this will generally require a greater investment of study time,
which may leave less time for other material. Thus, the setting of
the control criterion should be strategic in nature and influenced by
motivational and situational factors (cf. similar ideas with regard to
controlling the information that is reported from memory; e.g.,
Ackerman & Goldsmith, 2008a; Goldsmith & Koriat, 2008). Support for this idea comes from studies showing that increasing the
rewards for correct answers to particular items increases the
amount of time devoted to studying those items (Dunlosky &
Thiede, 1998), as does emphasizing accuracy over speed of learning (Lockl & Schneider, 2004; Nelson & Leonesio, 1988), as does
setting the “passing grade” for an upcoming test at 90% rather than
at 25% correct (LaPorte & Nath, 1976). With regard to potential
differences between OSL and OPL, if different target levels of
learning are being set for each media, perhaps because of different
levels of motivation or subjective comfort, one would then expect
to find correspondingly different subjective and objective levels of
performance between the two media.
A final component of the MLRP is control sensitivity: the extent
to which the learner’s control decisions are in fact sensitive to his
or her subjective monitoring. Essentially, this factor refers to the
tightness of the relationship between the control operation and the
monitoring judgment on which it is assumed to be based. In work
guided by their model of the strategic regulation of memory
1
In the metacognitive literature relating to the memorization of word
lists, this prediction is commonly termed Judgment of Learning (JOL).
Because in the present article our specific focus is on the study of texts, we
adopt the term POP to indicate the more complex, multilevel nature of
metacognitive assessments relating to the mastery of textual material.
ACKERMAN AND GOLDSMITH
20
Table 1
Summary Comparison of Metacognitive Learning Regulation Profiles (MLRP) for On-Screen
Learning (OSL) Versus On-Paper Learning (OPL), Across the Experiments
MLRP Component
Cognitive (encoding/storage)
1. Encoding efficiency
Metacognitive monitoring
2. Prediction of performance (POP)
3. Calibration bias
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4. Resolution
Metacognitive control
5. Control criterion
6. Control sensitivity
Performance
7. Self-regulated study time
8. Self-regulated test performance
Component source
Qualitative comparison result
Experiment 1
OSL ⫽ OPL
Experiment 1
Experiment 2 First online POP
Experiment 2 Terminal POP
Experiment 1
Experiment 2
Experiment 1
Experiment 2
OSL ⬎ OPL
OSL ⬎ OPL
OSL ⫽ OPL
OSL ⬎ OPL
OSL ⬎ OPL
OSL ⫽ OPL
OSL ⫽ OPL
Experiment 2 Online POP
Experiment 2 Online POP
OSL ⫽ OPL
OSL ⬍ OPL
Experiment 2 Terminal POP
Experiment 2
OSL ⬍ OPL
OSL ⬍ OPL
reporting, Koriat and Goldsmith (1996; for a review, see Goldsmith & Koriat, 2008) defined control sensitivity as the correlation
between subjective confidence in the correctness of a potential
answer (the monitoring output) and the decision whether to report
it or respond “don’t know” (the control decision). This correlation
was found to reach near-ceiling levels with healthy undergraduate
participants (Koriat & Goldsmith, 1996). In other studies with
special populations, however, this very high level of control sensitivity was reduced, offering insights into the nature of the cognitive and metacognitive deficits ensuing from old age (Pansky,
Koriat, Goldsmith, & Pearlman-Avnion, 2009) and mental illness
(Danion, Gokalsing, Robert, Massin-Krauss, & Bacon, 2001; Koren et al., 2004).
In the context of study regulation, control sensitivity can be
examined in terms of the consistency of the relationship between
POP [(or judgment of learning (JOL)] and the control of study
time. In general, a strong relationship has been found between JOL
Figure 1. Illustrative objective and subjective hypothetical learning
curves, based on the discrepancy reduction model, comparing conditions of
perfect calibration and overconfidence. Differences in knowledge level at
three different study termination points are also indicated: A – termination
with overconfidence, B – termination with perfect calibration, and C –
termination after a short, fixed study time (as in Experiment 1, here).
and the allocation of study time (e.g., Mazzoni, Cornoldi, &
Marchitelli, 1990), indicating a high level of control sensitivity. In
this context too, however, differences in control sensitivity may
underlie population differences in the effectiveness of study regulation. For example, Lockl and Schneider (2004) found that
although children as young as 6 years old can state that pairs of
related words are easier to learn than unrelated word pairs (Dufresne & Kobasigawa, 1989), nine-year-old children, but not
seven-year-old children, allocated study time in accordance with
item difficulty, suggesting lower control sensitivity in the younger
group (see also Koriat, Ackerman, Lockl, & Schneider, 2009).
Similarly, results from Dunlosky and Connor (1997) suggest that
older adults do not use online monitoring to allocate study time to
the same degree as younger adults do and that these allocation
differences contribute to age deficits in recall.
An underlying assumption of the discrepancy reduction model is
that control sensitivity is high—learners continue studying as long
as POP is below the criterion (target) level and stop studying as
soon as the criterion is reached (see Figure 1, stopping points A
and B). Thus, the measure of control sensitivity implied by this
model is the strength of the relationship between the ongoing POP
level during the study and the decision to continue or to stop
studying.
A somewhat different conception of control sensitivity is implied by an alternative model for control over study time, proposed
by Metcalfe and Kornell (2003, 2005). According to the region of
proximal learning model, people base their decision to stop studying on the perceived rate at which learning progresses, rather than
on a comparison of the absolute judgment level to a predefined
control criterion. When the learners perceive that they are gaining
knowledge at a rapid rate, they continue. When they feel that they
are no longer taking in information, they stop studying a particular
item and switch to another. Thus, monitoring of knowledge gaining rate is expected to be the basis for appropriate control decisions
(Son & Metcalfe, 2000). The region of proximal learning model
was used mainly to explain the finding that people allocate more
study time to intermediate-difficulty material than to the most
difficult material, as would be predicted by the discrepancy reduc-
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METACOGNITIVE REGULATION OF TEXT LEARNING
tion model. By assuming a different stopping rule that participants
should adhere to, this model also implies an alternative measure of
control sensitivity: the strength of the relationship between the
perceived rate of knowledge gain and the decision to continue or
to stop studying. Both ways of measuring control sensitivity were
examined in our research.
To sum up, a metacognitive analysis of the regulation of study
time yields a set of components— both cognitive and metacognitive—that potentially contribute to variance in the effectiveness of
text learning, and which may differ between populations and
learning conditions. In the following experiments, the MRLP
methodology, which provides an integrated assessment of these
components, will be used to identify and expose the possible
sources of text learning differences between OSL and OPL.
Overview of Experiments
The starting point for the experiments reported in this article is
the widespread preference of OPL over OSL, as discussed above.
This preference was found before over a large range of age and
experience levels, including young undergraduates who are used to
computer use and reading texts on screen (Buzzetto-More et al.,
2007).2 On this background, we report two experiments in which
we derived and compared the MLRPs of OSL and OPL. In both
experiments participants studied a set of expository texts, either
from a computer screen or from the printed page. Immediately
after studying each text, they predicted their test performance and
were tested before continuing to the next text. Because prior
metacomprehension research pointed to some ambiguity regarding
the type of monitoring reflected in global POP, memory of details
or higher order comprehension (Maki, 1995; Pieschl, 2009; Rawson et al., 2002; Thiede, Wiley, & Griffin, in press), we asked the
participants to provide two separate POPs, each targeted to one
specific aspect (Kintsch, 1998).
The purpose of Experiment 1 was to examine encoding efficiency and the accuracy of metacognitive monitoring under the
two study conditions, OSL and OPL. For this purpose, study time
was limited to a fixed and equal amount of time per text (see
Figure 1, stopping point C). By taking control of study time away
from the participants, the cognitive and metacognitive components
of OSL and OPL could be compared without potential contamination from the effectiveness of control decisions.
In Experiment 2, the time limit for studying each individual text
was removed and the participants were free to decide how much
time to allocate to each text (see Figure 1, stopping points A and
B). Because metacognitive differences in the efficiency of study
regulation could contribute to performance differences in Experiment 2 but not in Experiment 1, this allowed the unique contribution of self-regulation to performance differences between the two
study media to be revealed and the metacognitive components
underlying those differences to be examined.
Experiment 1
Perhaps the most natural account of the preference for OPL over
OSL that has been examined in research so far is that display
characteristics or presentation format simply make reading and
writing—and hence learning—more difficult when studying text
on a computer screen than when studying on paper. For example,
21
it might be that learning efficiency is affected by differences in
reading speed or in the ease of looking back and rereading text. By
this account, the primary source of media effects on learning
would be perceptual-cognitive, rather than metacognitive. If so, we
would expect to find a learning advantage for OPL over OSL, in
terms of increased encoding efficiency, under conditions in which
the allocation of study time is not under the learner’s control. This
issue was examined in Experiment 1. To shed additional light on
potential perceptual-cognitive factors, we examined whether media differences in learning efficiency would be tied to differences
in the frequency of using markup and note-taking tools (see Piolat,
Olive, & Kellogg, 2005; Spencer, 2006) and whether there would
be any differences in learning efficiency within the OSL group
between cathode ray tube (CRT) and liquid crystal display (LCD).
The display-type factor was included in the design in light of
studies finding differences between the two display types that
could potentially affect both objective and subjective aspects of
text learning (Kong-King & Chin-Chiuan, 2000; Marmaras, Nathanael, & Zarboutis, 2008; Menozzi, Lang, Näpflin, Zeller, &
Krueger, 2001; Sheedy, Subbaram, Zimmerman, & Hayes, 2005).
The texts were made long enough (2– 4 pages) to create potential
media differences in paging-scrolling difficulty as well, though
this factor was not systematically manipulated or analyzed.
Experiment 1’s design and procedure allowed us to measure
encoding efficiency and the accuracy of prediction of performance,
in terms of both calibration bias and resolution (components 1, 2,
3, and 4 in Table 1). Those MLRP components are best measured
under conditions that reduce the effects of self-regulation of study
time. For this purpose, a fixed amount of study time per text was
chosen (on the basis of pretesting), which allowed enough time to
study the main ideas of the text, while forcing most participants to
terminate their study before reaching the point at which they would
naturally do so.
Method
Participants
Seventy native Hebrew-speaking undergraduate social sciences
and humanities students (21 men, 49 women, mean age ⫽ 24.3
years) at the University of Haifa participated in the experiment
either for payment ($15) or for course credit (11 participants). The
participant recruitment notice specified that participants should not
have any type of learning disability, and students who reported
having learning disabilities on their personal data form were excluded from participating. The participants were randomly as2
To reinforce the motivation for the study, we conducted a study-media
preference survey (n ⫽ 126; 17– 61 years of age). The primary target
question was: “Assume that you need to read an article for serious study
(such as preparing for an exam, or for a lecture you are going to give), and
that the article was sent to you via the computer or that you found it on the
Internet. What would you usually do? (a) print out the paper or (b) read the
paper on screen.” Overall, 80% of the participants reported that they would
print the paper rather than read it on screen, attributing this preference to
ergonomic factors such as screen glare or eyestrain, poor spatial layout, and
clumsy markup and note-taking tools. Interestingly, there were no significant differences in the reported preferences of three different age groups
(17–20, 21–30, and 31– 61).
22
ACKERMAN AND GOLDSMITH
signed to OSL and OPL groups (n ⫽ 35 each). The OSL group was
further divided into CRT display (n ⫽ 17) and LCD display (n ⫽
18) conditions.
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Materials
The learning materials were six expository texts dealing with
various topics (e.g., the advantages of coal-based power stations
compared to other energy sources, adult initiation ceremonies in
various cultures, the importance of warming up before doing
strenuous athletic exercise). The texts were taken from Web sites
intended for reading on screen. They contained 1000 –1200 words
and included graphical or pictorial illustrations. When formatted
and presented as Microsoft Word documents, the texts were between two and four pages long. The format and number of pages
for each text were identical for on-screen and on-paper presentation. For each text, a test was devised consisting of ten fouralternative multiple-choice questions, five questions requiring
memory of details, and five questions requiring higher order comprehension, with both item types intermixed. An example of a
question that requires memory of details is the following: In which
decade did the “coal period” start in Israel? a) 1960s; b) 1970s; c)
1980s; d) 1990s. The answer (1980s) was explicitly mentioned in
the text. An example of a higher order comprehension question is
as follows: The electricity production process involves a fast
rotating rotor. What is the direct power source for this rotation? a)
gas exhaust generated by coal combustion; b) fast flowing water;
c) steam; d) hot air. The text explains that a high-temperature
vapor is produced by coal combustion, which at high pressure then
pushes a turbine that rotates the rotor (answer c).
The selection of texts and test questions for each text was based
on a pretest (n ⫽ 14). Eight texts were used as the initial text pool.
For each text, 30 questions were prepared, of which 10 questions
were selected. A “good” question was defined as one that without
reading the text first, the success rate was lower at least by 40%
than that achieved by answering the question with the text in hand.
In addition, the success rate for a text was required to be above
80% with the text in hand. This way we ensured that the questions
could be answered if based on the text and that the answers were
not obvious without reading the text first. If more than five good
questions of the same type were found for a single text, we used
the five questions that discriminated the best between answering
without reading the text and answering after reading the text. The
six texts with the best set of associated test questions were chosen
for inclusion in the experiment.
One shorter text, of 200 words, was selected by a similar
procedure and used as a practice text.
Apparatus
The computer displays were 17“ CRT or LCD (MAG Technology Co. Ltd., Models 786 FD and MS776K12, respectively), both
operating at 70 Hz, at a resolution of 1024 ⫻ 768. The OSL texts
were presented using Microsoft Word, 2003. The font was black,
12-point, Times New Roman, at 100% scale. For the OPL condition, the same texts were printed on A4-size paper (210 mm ⫻ 297
mm; the commonly used paper size in Israel).
Procedure
The experiment was administered to groups of two to six participants at a time, all OPL or all OSL, in a room with six computer
work stations. Thus, the physical room environment was the same,
regardless of study media. All participants read the general instructions from a printed booklet. The only substantive difference
between the sets of instructions pertained to the manner in which
annotations might be made during study: OSL participants were
provided with guidance about how to use the word-processing
tools available in Microsoft Word, including margin comments,
highlighting, underlining, and bold emphasis. All of the participants indicated that they were familiar with these basic markup
tools beforehand. OPL participants were provided with a pen and
a yellow highlighter as markup tools. The experimenter was present at all times.
Participants were told that they would be presented with a series
of texts for study. They would be given seven minutes to read each
text, during which they were allowed to make notes or mark the
text for emphasis, if they wished. They were told that they would
be given a multiple-choice test after each text, and that the test
would include questions requiring both memory of details and
higher order comprehension.
Except for the general instructions, in the OSL condition the
experiment was administered entirely by computer. A master program presented the instructions at each stage for each text: opening
each text in Microsoft Word for study, collecting POP judgments,
presenting the multiple-choice test, and recording the answers. In
the OPL condition, the same master program was used to display
the instructions on the computer screen. However, instead of
opening the text file for study on the computer, a window opened
up on the screen, indicating the title of the text to be studied next.
The participants would then take the printed text with that title
from the top of the pile of texts at their station and begin reading.
At the end of the allotted study time for each text, the OSL
participants saved their text file and closed Microsoft Word,
whereas the OPL participants simply placed the text face down on
their finished text pile. The participants then went on to the POP
phase, in which separate POP judgments were elicited for memory
of details and for higher order comprehension. The POP phase was
administered by computer for both media conditions: POP judgments were made by dragging an arrow along a continuous scale
between 25% and 100%. The question eliciting POP for memory
of details was phrased as follows: “What percentage of the questions that require memory of details do you expect to answer
correctly?” The same phrasing was used for the higher order
comprehension POP, except that “comprehension questions” replaced the “questions that require memory of details.” The instructions emphasized that the participants should evaluate their expected performance in light of the limited study time given for
each text.
Immediately after the POP phase, the multiple-choice test was
administered either on screen (for the OSL condition) or on paper
(for the OPL conditions). Five minutes were allotted for the test,
which allowed participants to answer the questions without time
pressure.
The experiment began with participants reading the instruction
booklet and a practice run of the entire task (study, POP, test)
using the shorter practice text. The allotted study and test times for
METACOGNITIVE REGULATION OF TEXT LEARNING
the practice run were five minutes and three minutes, respectively.
Its purpose was to familiarize the participants with the procedure
and the type of test questions that would characterize the texts to
follow. The set of six texts was then presented in one of two orders
counterbalanced between participants. The whole procedure, including instructions and practice text, took about 90 minutes.
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Results and Discussion
The main aim of this experiment was to compare the OSL and
OPL conditions in terms of encoding efficiency (test performance
without control of study time) and monitoring accuracy, both
calibration bias and resolution. Before doing so, however, we
checked whether the two potential control variables, display type
(manipulated) and use of markup and note-taking tools (measured), would need to be taken into account.
Potential Control Variables
Display type. Test scores and POP judgments of the OSL
group were equivalent for the two display types. Mean test score
(percent correct) was 62.9 for CRT and 59.1 for LCD, t(33) ⫽
1.09, p ⫽ .28, d ⫽ 0.38. Mean POP level was 69.8 for CRT and
72.2 for LCD, t ⬍ 1. Thus, in the following analyses both display
types were combined into a single OSL condition.
Use of markup and note-taking tools. The great majority of
participants (62 of 70) either marked five or six texts (20 OSL
participants and 20 OPL participants) or none or one of their texts
(12 OSL and 10 OPL participants). Among those who marked their
texts, OSL participants used color highlighting, bold text, underlining, inserted margin comments, and added summary notes to the
text; OPL participants made handwritten comments and used underlining and color-marker highlighting. The difference in the
number of marked texts between OSL (3.3) and OPL (3.9) was
examined by a Mann–Whitney U test, revealing no significant
difference between them, U ⫽ 547.00, p ⫽ .42. Most importantly
for our present concerns, when this variable was entered into the
design, it did not interact with study media in any of the subsequent analyses. Therefore, it was not included in the reported
analyses.
23
MLRP Components
Encoding efficiency. Encoding efficiency was defined earlier
as the amount of knowledge gain per time unit. Based on pretesting, the preliminary knowledge level (before study) was assumed
to be low and equivalent for the two groups of participants. Thus,
given a fixed and equal amount of study time per text in each
condition, test performance can be used as a comparable measure
of encoding efficiency between the two media. As seen in Figure
2A, the average overall test score (memory of details and higher
order comprehension questions combined) for the two media was
virtually identical (OSL: 61.0%; OPL: 60.7%; t ⬍ 1), indicating
equivalent encoding efficiency.
Prediction of performance. An overall POP measure was
calculated as the average of the POPs for memory of details and
higher order comprehension provided by each participant for each
text, corresponding to the overall test scores just reported. The
effect of study media on these subjective POP judgments can be
seen by examining Figure 2A. Despite the equivalent level of test
scores for the two study media, the combined POP was higher for
OSL (71.0%) than for OPL (65.6%), t(68) ⫽ 1.99, p ⫽ .05, d ⫽
0.48. Thus, although objectively there was no observed difference
in encoding efficiency between the two media, the OSL participants nevertheless felt subjectively that they had learned the material better than did their OPL counterparts.
Calibration bias. To examine more directly the degree of
correspondence between subjective and objective learning, calibration bias scores were calculated as the difference between the
mean overall POP and test score of each participant, with a
positive score indicating overconfidence and a negative score
indicating underconfidence. As reflected in Figure 2A, both of the
groups exhibited overconfidence. Taking into account the manner
in which the POP judgments were elicited from the participants, a
two-way ANOVA, Study Media ⫻ Question Type (memory of
details vs. higher order comprehension) was performed on the bias
scores. The main effect of study media was significant, F(1, 68) ⫽
2.50, MSE ⫽ 364.40, p ⬍ .05, ␩2p ⫽ .04, indicating greater
overconfidence in the OSL condition (10.1) than in the OPL
condition (5.0). A main effect of question type was also found,
F(1, 68) ⫽ 43.59, MSE ⫽ 87.27, p ⬍ .0001, ␩2p ⫽ .39, reflecting
Figure 2. Mean combined prediction of performance (POP) and test scores in Experiment 1 under fixed study
time (A) and in Experiment 2 under self-regulated study time (B). Error bars represent standard error of the
mean.
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24
ACKERMAN AND GOLDSMITH
greater overconfidence for the questions concerning higher order
comprehension (test score ⫽ 60.8, POP ⫽ 73.5, calibration bias ⫽
12.7) than for the questions concerning memory of details (test
score ⫽ 60.8, POP ⫽ 63.1, calibration bias ⫽ 2.3, not significantly
different from zero). There was no interaction (F ⬍ 1), indicating
that the greater overconfidence for OSL than for OPL was not
limited to a particular question type.
Resolution. As explained earlier, whereas calibration bias
reflects absolute monitoring accuracy, monitoring resolution reflects relative monitoring accuracy—the extent to which one’s
subjective judgments discriminate between higher and lower levels
of actual performance. A common index of monitoring resolution
in item-based (list-learning) memory research is the Goodman–
Kruskal Gamma correlation between the metacognitive judgment
and the correctness of each individual item, calculated within
individuals (Nelson, 1984). This index has sometimes been extended to examine the monitoring of text comprehension, by
treating each individual text as an item (e.g., Thiede et al.,
2003). We did this separately for the memory of details and
higher order comprehension questions and found very low
correlations, with no significant difference between the two
media for either memory of details (OPL ⫽ .07; OSL ⫽ .10;
t ⬍ 1) or higher order comprehension (OPL ⫽ .11; OSL ⫽ .18;
t ⬍ 1).
Gamma correlations become quite unstable when the number of
items is small, particularly when there are “ties” on one or both
variables, which further reduce the number of items that are
actually included in the calculation (for other recent criticisms of
gamma, see Benjamin & Diaz, 2008; Masson & Rotello, 2009).
The small number of texts also precludes the use of other standard
measures, such as da or d⬘ (Masson & Rotello, 2009). For these
reasons, we conducted an additional check by calculating the
within-participant Spearman correlation between POP and actual
performance on each text. Again we found very low correlations, with no significant difference between the two media for
either memory of details (OPL ⫽ .08; OSL ⫽ .14; t ⬍ 1) or
higher order comprehension (OPL ⫽ .09; OSL ⫽ .18; t ⬍ 1).
We suspect that the low values observed for both Gamma and
Spearman correlations reflect insufficient within-participant
variance to meaningfully assess monitoring resolution in this
experiment.3
To sum up: First, if the effects of technology-related factors
such as display properties, mark up tools, and ease of scrollingpaging on encoding efficiency are the main source of differences
between the two study media, we would expect to find a difference
in test performance between OSL and OPL when study time is
fixed and equated. The fact that no such difference was found
counts against this possibility—a conclusion that is reinforced by
the lack of effect of display type (CRT vs. LCD). Of course, these
results do not rule out the possibility that display and software
properties could affect learning efficiency in other contexts (cf.
Kong-King & Chin-Chiuan, 2000; Menozzi et al., 2001; Sheedy et
al., 2005). Second, the observed difference in calibration bias—
greater overconfidence under OSL than OPL—suggests that there
may be metacognitive differences between the two study media,
whose effects on test performance might emerge when study time
is self-regulated.
Experiment 2
As explained earlier, Experiment 2 used essentially the same
materials and procedure as Experiment 1, but with one important
difference: Here, the participants could decide for themselves how
much time to spend on each text within a loose, global time frame.
The main question was whether a difference between OSL and
OPL in test performance would now emerge, attributable to differences in the effectiveness of study-time regulation between the
two study media.
The combined data from the two experimental procedures (fixed
study time in Experiment 1; self-regulated study time in Experiment 2) provided information regarding the MLRP components of
encoding efficiency, prediction of performance, calibration bias,
resolution, self-regulated performance, and self-regulated study
time. In addition, to allow a more fine-grained examination of the
quality of metacognitive control, information regarding ongoing
changes in subjective knowledge level during study was also
collected: half the participants in Experiment 2 provided “online”
POP judgments during study in addition to their final POP judgments. The POP judgments elicited before and after the decision to
stop studying were used to estimate the control criterion adopted
by each participant and to examine control sensitivity in terms of
the strength of relationship between online POP and the decision to
stop studying.
Method
Participants
Seventy-four native Hebrew-speaking undergraduates without
learning disabilities (mean age ⫽ 24.6 years; 24 males and 50
females) participated in the experiment, either for payment or for
course credit. Half were randomly assigned to the OPL condition
and half to the OSL condition. Nineteen participants in each
condition received the terminal-POP procedure, whereas the remaining 18 received the online-POP procedure.
Materials and Apparatus
The same software and materials used in Experiment 1 were
used again in this experiment. Because Experiment 1 yielded no
effect of computer display type, only LCD displays (same as in
Experiment 1) were used in this experiment.
Procedure
The procedure was similar to the one used in Experiment 1, with
study media again manipulated as a between-participants variable.
The participants studied each text, predicted their performance for
memory of details and for higher order comprehension, and were
3
Calculating gamma and Spearman correlations using the overall POP
and test scores (mean of memory of details and higher order comprehension questions) yielded a similar picture: Gamma correlations averaged
0.16 for OSL and 0.05 for OPL, with no significant difference between the
two study media, t(68) ⫽ 1.04. Spearman correlations averaged 0.19 for
OSL and 0.06 for OPL, with no significant difference between the two
study media, t(68) ⫽ 1.28, p ⫽ .21, d ⫽ 0.30.
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METACOGNITIVE REGULATION OF TEXT LEARNING
then tested by multiple-choice questions. For the terminal-POP
group, the only difference from Experiment 1 was that in this
experiment participants managed their study-time allocation freely
within a 90-min global time frame for studying all six texts. It was
explained to the participants that this meant about 15 minutes per
text, including text study, POP elicitation, and test. In the few
cases (two OSL and three OPL participants) in which participants
were still studying the fifth text after 70 minutes had gone by, they
were asked to finish studying the text they were working on
without time pressure, and the last (sixth) text was waived.
The online-POP group went through the same procedure, but in
addition they were required to pause their studying every three
minutes to provide a current POP judgment, for both memory of
details and higher order comprehension, in addition to the terminal
POP judgments provided after study was completed. The instructions emphasized that the online POPs at each point in time should
take into account how much of the text had been studied so far, and
how much still remained to be learned. Study interruptions for
online POP were expected to prolong the overall study time, so
although the same global 90-min time limit was presented in the
instructions, for the online-POP group it was not enforced.
Results and Discussion
A comparison between the two methods of POP elicitation,
online and terminal POP, revealed no differences in any of the
dependent measures reported below. In particular, there was no
interaction between POP elicitation method and study media on
test scores or terminal POPs (all Fs ⬍ 1). Thus, until reaching the
analyses of control criterion and control sensitivity, unless specified otherwise, the data analyses were collapsed across the groups.
Use of Markup and Note-Taking Tools
As in Experiment 1, most of the participants (65 of 74) either
marked five or six of their texts (31 OSL and 19 OPL participants)
or none or one of the texts (4 OSL and 11 OPL participants).
Analysis of the number of marked texts per study media by a
Mann–Whitney U test indicated that in this experiment there was
a greater tendency for OSL participants (4.7) to mark their texts
than for OPL participants (3.5), U ⫽ 418.00, p ⬍ .01. This finding
is somewhat surprising, because people, including our survey
participants, usually report that one of the reasons for their reluctance to study on screen is that the markup and note-taking tools
are harder to use. Importantly, when frequency of markup was
included as an additional factor, it did not interact with study
media in any of the subsequent analyses.
25
F(5, 150) ⫽ 2.05, MSE ⫽ 2.35, p ⬍ .08, ␩2p ⫽ .06. A post hoc LSD
test indicated that the only significant differences were between
the first text, which was studied for the longest amount of time
(10.1 min), and the rest of the texts (9.2 min each).
Comparison of study time per text between the two media
showed that less study time was invested by OSL participants (9.1
min) than by OPL participants (10.0 min), though the difference
only approached significance, t(36) ⫽ 1.81, p ⬍ .08, d ⫽ 0.63.
This trend accords with the results for calibration bias reported
below, perhaps reflecting the control consequence of overconfidence in monitoring. Note also that the participants studied each
text for an average of 9.6 minutes, significantly longer than the
seven minutes allowed in Experiment 1, t(37) ⫽ 10.63, p ⬍ .0001,
d ⫽ 1.72. This reinforces our earlier assumption that the participants in Experiment 1 would generally not have reached their
natural study-termination point in the fixed allotted time (see
Figure 1, stopping point C).
Performance.
Test scores in this experiment, under selfpaced study, were lower for OSL (63.2%) than for OPL (72.3%),
t(72) ⫽ 3.34, p ⫽ .001, d ⫽ 0.79 (see Figure 2B). To compare the
pattern under self-regulated learning (Experiment 2) and fixed
study time (Experiment 1), a two-way ANOVA, Experiment ⫻
Study Media, was performed on the test scores. There was a main
effect of experiment, F(1, 140) ⫽ 12.68, MSE ⫽ 137.66, p ⫽ .001,
␩2p ⫽ .08, a main effect of study media, F(1, 140) ⫽ 5.12, MSE ⫽
137.66, p ⬍ .05, ␩2p ⫽ .04, and a significant interaction F(1,
140) ⫽ 5.80, MSE ⫽ 137.66, p ⬍ .05, ␩2p ⫽ .04. The significant
interaction indicates that the advantage of OPL over OSL observed
under self-regulated learning in Experiment 2 does in fact differ
from the null effect under fixed study time in Experiment 1.
Prediction of performance. Figure 2B shows that despite the
performance difference observed in this experiment, overall terminal POP did not differ between the two study media, t ⬍ 1. For
the OPL participants, however, POP was higher in Experiment 2
than in Experiment 1, t(70) ⫽ 3.28, p ⬍ .01, d ⫽ 0.78, reflecting
the increase in actual test performance in that condition. There was
no difference in POP between experiments for the OSL participants, t ⬍ 1, corresponding to the lack of difference in test
performance in that condition. This pattern suggests that POP is
sensitive to differences (or lack of difference) in learning level.
Examination of online POPs provided by half of the participants
(n ⫽ 18 in each media) allowed us to compare the subjective
learning curves between OSL and OPL. Figure 3 plots the mean
online POP at each elicitation point separately for each study
media. The overall shape of the plots fits the theoretical learning
curve presented in Figure 1. For both media, there was marked
subjective progress in the initial learning stages, with decelerated
progress as study continued. A two-way ANOVA, POP Elicitation
MLRP Components
Study time. Study time was a meaningful measure only for
the terminal-POP group (n ⫽ 38).4 The global time limit for
studying all of the texts was 90 min. The actual total study time,
excluding the participants whose sixth text was waived, averaged
76.6 min, suggesting that studying was finished smoothly without
any pressure. To verify that the global time-frame had not caused
these participants to rush at the end of the session, a one-way
ANOVA was performed to examine the effect of Serial Position
(6) on study time per text. This analysis revealed a marginal effect
4
Note that participants in the online-POP condition had to interrupt their
study of each text up to four times to provide the online POP judgments.
This added about two minutes to the average study time per text (M ⫽ 11.4
min for online-POP vs. 9.5 min for terminal-POP) as well as a substantial
amount of additional variance (SD ⫽ 1.96 min for online-POP vs. 1.48 min
for terminal-POP). Moreover, the time lost in making and switching
to/from the POP judgments could not be separated from the time spent in
actual study of the text. For these reasons, only the terminal-POP group
was used in the analysis of self-regulated study time as a dependent
measure.
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26
ACKERMAN AND GOLDSMITH
Figure 3. Mean overall prediction of performance (POP) per elicitation
point for OPL versus OSL in the online-POP condition of Experiment 2.
Number of participants contributing to each data point was n ⫽ 18, except
for the fifth data point for which n ⫽ 12 for OSL and n ⫽ 13 for OPL. Error
bars represent standard error of the mean.
Point (4) ⫻ Study Media was performed on points 1– 4 in which all
of the participants had data. It revealed a main effect of elicitation
point, F(3, 102) ⫽ 116.12, MSE ⫽ 47.26, p ⬍ .0001, ␩2p ⫽ .77, and
a significant interaction with the media, F(3, 102) ⫽ 8.19, MSE ⫽
47.26, p ⬍ .0001, ␩2p ⫽ .19. A comparison between the two study
media at each elicitation point revealed a significant difference
only at the first elicitation point, t(34) ⫽ 2.54, p ⬍ .05, d ⫽ 0.87
and nonsignificant differences at all subsequent points. OSL participants predicted their performance after three minutes to be at
48%, whereas OPL participants were more moderate in their
judgments (36%). This unfounded inflation of predictions for OSL
after a short fixed amount of study time accords with the POP
difference observed under fixed study time in Experiment 1.
Calibration bias. As in Experiment 1, we compared calibration bias between the two study media including question type as
an additional factor. The two-way ANOVA revealed again a main
effect of study media, F(1, 72) ⫽ 6.78, MSE ⫽ 357.87, p ⫽ .01,
␩2p ⫽ .09, with larger calibration bias for OSL (10.4) than for OPL
(2.3; not significantly different from zero). A main effect of
question type was again observed, F(1, 72) ⫽ 31.34, MSE ⫽
75.69, p ⬍ .0001, ␩2p ⫽ .30, reflecting greater overconfidence for
higher order comprehension (test score ⫽ 67.8; POP ⫽ 78.2;
calibration bias ⫽ 10.3) than for memory of details (test score ⫽
67.7; POP ⫽ 70.1; calibration bias ⫽ 2.3, not significantly different from zero). Finally, as in Experiment 1, there was again no
interaction between the effects of study media and question type
(F ⬍ 1), indicating that the greater overconfidence for OSL than
for OPL was not limited to a particular question type.
Resolution. As in Experiment 1, Gamma correlations were
very low, yielding no difference between the study media for either
question type (memory of details: OPL ⫽ .10, OSL ⫽ .21; t ⬍ 1;
higher order comprehension: OPL ⫽ .12, OSL ⫽ .03; t ⬍ 1). A
similar pattern was found using Spearman correlations (memory of
details: OPL ⫽ .08, OSL ⫽ .21; t(72) ⫽ 1.18, p ⫽ .24, d ⫽ 0.27;
higher order comprehension: OPL ⫽ .07, OSL ⫽ .03; t ⬍ 1). Thus,
as in Experiment 1, there is no evidence of media differences in
monitoring resolution, though once again we suspect that the low
correlations stem from low within-participant variance in POP and
in actual performance between texts.
Control criterion. The equivalent levels of terminal POP
between the two study media reported earlier suggest that the same
target level of knowledge may have been adopted as a control
criterion. The data from the online-POP group was used to examine this possibility more stringently. According to the discrepancy
reduction model, the terminal POP level for each studied text
should be located just at or above the control criterion, whereas the
preceding online POP, provided just before study termination,
should be located below the criterion. Thus, adapting the computational procedure used by Koriat and Goldsmith (1996) to estimate the control criterion in memory reporting, we identified for
each participant the POP level (average of memory of details and
higher order comprehension questions) that would be below all
(most) of the terminal POPs and above all (most) of the immediately preceding POPs provided for the set of texts studied by that
subject. The chosen criterion estimate was the candidate POP level
that maximized the “fit rate” (cf. Koriat & Goldsmith, 1996),
defined as the percentage of all POPs (two times the number of
studied texts) which were in fact above or below the candidate
criterion level in accordance with the discrepancy reduction model.
If a range of potential criteria yielded an equivalent fit rate, the
midpoint of the range was used as the best point estimate.
Using this procedure, the mean estimated control criterion for
participants in the OSL condition (M ⫽ 68.5) did not differ from
that in the OPL condition (M ⫽ 69.5), t ⬍ 1. The criterion fit rates
were also equivalent for the two study media (77% for OPL and
80% for OSL), t ⬍ 1. This finding reinforces the conclusion
implied by the equivalent terminal-POP levels that the same target
level of knowledge was strived for, regardless of the study media.
Control sensitivity. The online-POP procedure also allowed
control sensitivity to be compared between the two study media.
According to the discrepancy reduction model, all POPs provided
during the study process should be lower than the one produced
after the decision to stop studying. Thus, we calculated for each
participant the percentage of texts for which the highest POP
(average of memory of details and higher order comprehension
questions) was accompanied by the decision to stop studying. The
percentage for OSL (M ⫽ 79.4%) was significantly lower than for
OPL (M ⫽ 98.2%) t(34) ⫽ 2.45, p ⬍ .05, d ⫽ 0.85. By this
analysis, the decision to stop studying was less consistently related
to the subjective monitoring in OSL than in OPL. In fact, whereas
control sensitivity was virtually at ceiling and with very little
variance under OPL (16 of 18 participants yielding a sensitivity
score of 100%; range: 83–100%), there was a much higher degree
of interindividual variability in control sensitivity under OSL, as
can be seen by the much larger standard deviation (10 of 18
participants yielding a sensitivity score of 100%; range: 0 –100%).
We also analyzed control sensitivity with respect to the region
of proximal learning model, which holds that change in POP,
rather than the absolute level of POP, is the basis for study
termination. To do so, we identified for each studied text of each
participant, the minimum difference between two consecutive
POPs (again using the average of memory of details and higher
order comprehension questions). For each minimum difference,
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METACOGNITIVE REGULATION OF TEXT LEARNING
the learner’s decision at that point, whether to continue or to stop
studying, was tabulated. The percentage of minimum differences
that were accompanied by a decision to stop studying (i.e., for
which the second of the two consecutive POPs was a terminal
POP) was significantly lower for OSL (M ⫽ 36.3%) than for OPL
(M ⫽ 59.8%) t(34) ⫽ 2.77, p ⬍ .01, d ⫽ 0.95. Thus, the
application of the region of proximal learning model provides a
result that converges with the result based on the discrepancy
reduction model. By both analyses, the decision to stop studying
was more “erratic”—less related to the monitoring output—for
OSL than for OPL.
In sum, the main finding of this experiment was that in contrast
to the equivalent test performance under fixed study time, performance under self-paced study was lower for OSL than for OPL.
Moreover, the lower test performance of OSL was accompanied by
significant overconfidence with regard to predicted performance,
whereas OPL participants monitored their performance more accurately. This overconfidence difference was consistent with other
differences that would be expected under the discrepancy reduction model: the somewhat shorter study time and the ensuing lower
level of actual learning for OSL relative to OPL. The control
criterion (norm of study) was found to be equivalent for the two
study media. This suggests that the participants intended to achieve
the same level of knowledge, regardless of the study media,
leading us to reject a goal-setting explanation for the performance
difference between OSL and OPL. Control sensitivity, on the other
hand, was weaker for OSL than for OPL, implicating this factor as
an additional potential source of lower OSL performance under
conditions of self-regulated study.
General Discussion
The technological advances of the last few decades have led
investigators to examine the potential benefits of novel methods of
instruction (e.g., Chou & Liu, 2005; Chumley-Jones, Dobbie, &
Alford, 2002; Macedo-Rouet, Rouet, Epstein, & Fayard, 2003;
Mayer, 2003; Metcalfe, Kornell, & Son, 2007), as well as the
preconditions for taking advantage of these new methodologies
(Coiro, Knobel, Lankshear, & Leu, 2008; Eshet-Alkalai, 2004).
However, citing a list of studies of self-regulated learning with
hypermedia, Azevedo and Cromley (2004) concluded that “students have difficulties benefiting from hypermedia environments
because they fail to engage in key mechanisms related to regulating their learning” (p. 523). In the present study we took a step
back from the more novel aspects of the new learning technologies, examining the impact of on-screen text presentation on the
more basic processes of text learning. This simplification allowed
us to examine whether metacognitive learning regulation difficulties are found even in simpler computerized environments, without
the extra challenges presented to the learner by advanced study
techniques. We assume that the basic processes of reading and
remembering expository texts on screen are essential building
blocks of the more complex learning and regulatory processes that
operate in more technologically sophisticated learning environments (Shapiro & Niederhauser, 2004). Thus, regardless of whatever other types of media differences in learning processes there
might be, the basic differences found here should contribute to
differences in almost all computer-learning environments.
27
In addition to shedding light on potential differences in the
processes underlying text learning on screen versus on paper, an
additional and independent aim of the present article was to put
forward the metacognitive framework in general, and the MLRP
methodology in particular, as a useful approach to the examination
and analysis of objective and subjective differences in learning
processes. In what follows, we first discuss how the MLRP methodology was used in the present study to uncover such differences
in the underlying learning processes between the two study media,
and then move on to focus on the findings themselves and their
implications for the learning of texts in computerized
environments.
The MLRP Methodology
The MLRP is proposed as a general methodology for analyzing
study regulation in terms of its cognitive and metacognitive components, enabling the concurrent examination of the potential
contributions of these components— contributions that might not
be considered otherwise. The methodology is essentially a synthesis of methods based on two theoretical models of study time
regulation, the discrepancy reduction model and the region of
proximal learning model, and on methods developed to examine
the strategic regulation of memory retrieval and reporting. All of
these emphasize the causal relationships between metacognitive
monitoring and control operations and the impact that these operations have on actual performance (e.g., Benjamin, Bjork, &
Schwartz, 1998; Goldsmith & Koriat, 2008; Kornell & Metcalfe,
2006; Metcalfe & Finn, 2008; Nelson & Dunlosky, 1991; Thiede
et al., 2003). We now discuss each MLRP component in turn (see
Table 1) and consider the information that is gained by its assessment.
Encoding efficiency. In Experiment 2, under self-regulated
study, test performance was lower for OSL than for OPL.
However, this finding alone does not indicate the reason for the
difference between the two study media. To examine potential
differences in encoding efficiency, it is necessary to take away an
important “degree of freedom” that learners usually have— control
over the allocation of study time. This was done in Experiment 1.
In that experiment, under a short and fixed study time, OSL and
OPL performance was equivalent. This finding may imply equivalent learning processes in the two media, but it could also reflect
the offsetting effects of differential reading speed, attention, fatigue, and many other uncontrolled factors. Whatever the underlying reasons for the equivalent encoding efficiency, the important
implication is that although people are reluctant to study on screen,
they can potentially do so as efficiently as on paper. This finding
provides an important insight into the potential source of differences under more natural study conditions, in which learners
control the amount of time allocated to each text, pointing to the
role of self-regulated control of study time and the contribution of
such control to learning performance.
Monitoring. In metacomprehension studies, participants are
typically asked to provide POP only at the end of text learning.
Under self-regulated study, however, such POPs may tap a combination of subjective encoding efficiency and study-time regulation efficiency. According to the discrepancy reduction model,
learners can compensate for low assessed knowledge by investing
more study time, and this is expected to bring them, at least
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28
ACKERMAN AND GOLDSMITH
subjectively, to a similar level of performance (their control criterion) for all texts. Indeed, the variability of terminal POPs in
Experiment 1 was larger than that of the terminal POPs in Experiment 2 (Experiment 1: Mean SD ⫽ 10.1; Experiment 2: Mean
SD ⫽ 8.5, t(142) ⫽ 2.05, p ⬍ .05, d ⫽ 0.35). The MLRP
methodology taps the monitoring process before compensation by
study-time allocation can take place, either by terminating the
study early after a fixed amount of time (terminal POP; Experiment 1) or by eliciting POP early during self-regulated study
(online-POP; Experiment 2). In the present study, both methods
revealed a difference that was otherwise hidden: although there
was no difference between study media in terminal metacognitive
predictions under self-regulated study, OSL predictions were
higher than OPL predictions when elicited in the early stage of
study, before study-time regulation could take place.
As in the general metacomprehension literature, monitoring
accuracy is examined within the MLRP methodology in both
absolute (calibration bias) and relative (resolution) terms. The
results pertaining to calibration bias were quite consistent between
the two experiments: OSL was accompanied by a greater degree of
overconfidence than OPL. Based on the discrepancy reduction
model, this difference in overconfidence should have a causal
effect on the allocation of study time (see Figure 1, earlier).
With regard to relative monitoring accuracy, the examination of
POP resolution tends to be problematic in the context of text
learning research, because of the relatively small number of judgments that can be collected and included in the calculation of the
measures. In the present research, we assessed POP resolution
using the Goodman-Kruskal gamma and the Spearman correlations. Both measures indicated very low levels of resolution,
probably because of the small number of texts and their similar
levels of difficulty.
One change from the common metacomprehension procedure,
relevant to the measurement of both relative and absolute monitoring accuracy, was the elicitation of separate POP judgments for
the two question types, memory of details and higher order comprehension. The separation of these POP judgments was expected
to focus participants’ attention on the unique aspects of each
knowledge type, thereby improving monitoring accuracy. We cannot know whether the separate elicitation had any effect on POP
accuracy. However, we can point to the fact that POPs for higher
order comprehension were characterized by greater overconfidence than POPs for memory of details. One possible basis for
such differences is that higher order comprehension judgments
might reflect an evaluation of general ability (Zhao & Linderholm,
2008), whereas judgments regarding memory of details might be
related more to the specific material (cf. theory-based vs.
experience-based cues; Koriat, 1997). In this case, we would
expect low within-participant variability in POPs for higher order
comprehension relative to POPs for memory of details across the
six texts studied by each participant. To examine this idea, we
compared the mean within-participant standard deviation for the
two question types (see Baker & Dunlosky, 2006). We found that
the variability in POPs for memory of details was indeed larger
than in POPs for higher order comprehension, though the difference between them was small [Experiment 1: Memory detail POP
M ⫽ 11.61, SE ⫽ 0.59; higher order comprehension POP M ⫽
10.07, SE ⫽ 0.50; t(69) ⫽ 3.49, p ⫽ .001, d ⫽ 0.42. Experiment
2: Memory detail POP M ⫽ 10.12, SE ⫽ 0.57; higher order
comprehension POP M ⫽ 8.33, SE ⫽ 0.60; t(73) ⫽ 4.53, p ⬍
.0001, d ⫽ 0.53].
Control. Turning now to the control components, the MLRP
methodology allows the level of the control criterion (norm of
study under the discrepancy reduction model) to be inferred either
from the terminal level of POP under the uninterrupted selfregulated study procedure, or by identifying the POP level that best
differentiates the terminal level of POP from the preceding POP
levels, under the online-POP procedure. No differences were
found between the two study media in the level of terminal POP
per se or in the criterion estimates based on the online POP
procedure.
A second aspect of control that was examined is control sensitivity. As explained above, it is assumed by both of the theoretical
models that study termination is tightly related to subjective monitoring, although by different stopping rules. By both of the models, control sensitivity was weaker for OSL than for OPL. As
mentioned in the introduction, in the context of memory reporting,
control sensitivity of healthy young adults is generally at ceiling
(e.g., Koriat & Goldsmith, 1996), whereas lower control sensitivity
may be diagnostic of impairment associated with schizophrenia,
psychoactive drugs, and normal aging processes (see review in
Goldsmith & Koriat, 2008; Pansky et al., 2009). The present
finding of reduced control sensitivity under OSL in normal young
adults demonstrates the potential value of this measure for exposing situational control impairments as well.
The investigation of media differences in study regulation is
highly relevant for many applications. However, we also believe
that the examination of these differences provides a good “case
study” to highlight the general potential utility of the MLRP
methodology, because it generates an intriguing situation in which
equivalent groups of participants study the same set of materials
but have different qualities of subjective experience. From a metacognitive perspective, this difference in subjective experience
should have consequences for study regulation, which in turn
should have consequences for the ultimate level of learning as
measured by test performance. Thus, the comparison of the
MLRPs between OSL and OPL illustrates a general approach to
analyzing and examining differences in text learning processes,
beyond the common focus on student characteristics and aspects of
the study materials.
Metacognitive Learning Regulation on Screen Versus
on Paper
After focusing on the potential contribution of the MLRP methodology to the analysis of study regulation in general, we turn now
to discuss the more specific insights that can be gained by comparing the MLRPs of OSL and OPL (see Table 1, column 3).
Interestingly, the common preference of OPL over OSL appears to
be justified, because test performance was indeed lower for OSL
under natural, self-regulated study conditions (Experiment 2).
Such differences were implied in previous studies and explained in
terms of display factors (e.g., Garland & Noyes, 2004) or difficulties with the use of markup and note-taking tools on screen relative
to on paper (O’Hara & Sellen, 1997). However, our results discount this as the main difference between the two media: First,
markup and note-taking tools were used to a similar extent in both
media in Experiment 1 and even more for OSL than for OPL in
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METACOGNITIVE REGULATION OF TEXT LEARNING
Experiment 2. Second, characteristics of the computer screen and
software did not prevent participants from achieving equivalent
performance levels on screen and on paper in Experiment 1 (see
also Annand, 2008). The findings of no difference in encoding
efficiency between OSL and OPL and the emergence of a performance difference only under self-regulated study time suggest that
the efficiency of study regulation is the critical factor underlying
the observed performance difference. Of course, as mentioned
earlier, the generality of this conclusion will need to be examined
further in future research.
Conceivably, test performance differences between OSL and
OPL could also reflect differences in test media rather than in
study media. However, the finding of equivalent test performance
in Experiment 1 counts against the possibility that media effects on
test processes are responsible for the observed performance differences in Experiment 2, whose test conditions were identical to
those in Experiment 1. Nevertheless, it is worth considering the
possibility that differences in test media might also affect metacognitive processes involved in the retrieval and reporting of one’s
answers (cf. Goldsmith & Koriat, 2008; Higham, 2007), a possibility that deserves further examination.
Turning to possible regulatory differences between the study
media, one potential difference might be that participants have an
initial reluctance toward studying on screen and therefore do not
intend to achieve the same performance level as when studying on
paper. This possibility was discounted, however, by the finding of
equivalent estimated control criteria for the two media, indicating
that the participants in both conditions intended to achieve similar
levels of learning.
Metacognitive processes were found to differ between the two
study media in two aspects. First, overconfidence was consistently
greater for OSL than for OPL. A possible explanation for this
difference is that the learners who studied on screen faced a more
difficult learning situation. Studying difficult materials is known to
increase overconfidence relative to easier materials (“hard-easy
effect,” Lichtenstein, Fischhoff, & Phillips, 1982). However, the
hard-easy effect should reflect a pattern in which there is a large
difference in performance between OSL and OPL, with a smaller
difference in the subjective estimation of knowledge. In contrast,
our results yielded equivalent performance, with higher POP for
OSL than for OPL (in Experiment 1), indicating that OSL was not
objectively harder than OPL. Thus, we conclude that the OSL
overconfidence was not related to objective task difficulty.
Greater overconfidence for OSL than for OPL is especially
puzzling in light of the common reluctance to study on screen (see
Introduction). In fact, such reluctance might be expected to be
expressed in relative underconfidence. Thus, there seems to be
incongruity between the overall attitude toward OSL versus OPL
and the metacognitive judgments that are made with respect to
specific studied texts. This incongruity may perhaps be resolved in
terms of the difference between metacognitive (first-order) and
meta-metacognitive (second-order) judgments. In the context of
list-learning memory tasks, for example, Dunlosky, Serra, Matvey,
and Rawson (2005) asked participants to make second-order metacognitive judgments (called SOJs) which expressed their confidence in the accuracy of their first-order metacognitive judgments
(JOLs). The second-order confidence judgments were found to be
higher for extreme JOLs than for intermediate-level JOLs and for
delayed JOLs compared with those made immediately after study.
29
In both cases, the second-order judgments were in fact sensitive to
differences in the accuracy of the first-order (JOL) judgments. In
a similar vein, it may be that one’s overall subjective feeling
toward OSL represents a general meta-metacognitive judgment at
a more global level—in this case reflecting the perceived overall
quality of one’s own metacognitive monitoring and control processes when studying on screen as opposed to on paper. If learners
do monitor the reliability of their own metacognitive processes and
perceive these processes as generally less reliable on screen than
on paper (as indicated in the present results), then this metametacognitive judgment could lead to a reluctance to study on
screen, and would in fact appear to reflect the observed performance differences between the two media better than the firstorder memory and comprehension monitoring.
The perceived unreliability of one’s own monitoring during
on-screen learning might also explain the second component that
was found to differ between the media— control sensitivity. If
one’s monitoring is perceived as less reliable, one might tend less
to base one’s study-time control decisions on that monitoring.
Following up on these ideas, the decision to print digitally presented material before study might be viewed as a metametacognitive control decision that transfers the study materials to
the more subjectively reliable context of paper learning. This
interpretation, though highly speculative, suggests the need to
consider factors that affect more global (second order) selfevaluations of one’s metacognitive abilities in particular contexts,
which in turn may influence more specific (first order) metacognitive monitoring and control behaviors.
Why are some metacognitive processes less effective on screen?
This too might perhaps be attributable in part to higher order
metacognitive beliefs. Consider a related idea from the literature
on age-related study deficits: It has been suggested that such
deficits are related to self-referent beliefs about one’s ability to
effectively mobilize cognitive resources (e.g., Bandura, 1989).
Older adults believe that they are less able than younger adults to
recruit the needed resources when faced with a cognitive task and
so may be less likely to do so (e.g., Berry, West, & Dennehey,
1989; Miller & Lachman, 1999; Stine-Morrow, Shake, Miles, &
Noh, 2006). Similarly, people appear to perceive the printed-paper
medium as best suited for effortful learning, whereas the electronic
medium is better suited for fast and shallow reading of short texts
such as news, e-mails, and forum notes (Shaikh, 2004; Spencer,
2006; Tewksbury & Althaus, 2000). The common perception of
screen presentation as an information source intended for shallow
messages may reduce the mobilization of cognitive resources that
is needed for effective self regulation.
Research on metacomprehension has found that as people engage in more effortful processing, both performance and relative
monitoring accuracy benefit (Rawson et al., 2000; Thiede et al.,
2003; Thiede, Dunlosky, Griffin, & Wiley, 2005). It may be that
overcoming overconfidence bias is also facilitated by cognitive
effort and deep processing, by leading to a reliance on more
appropriate monitoring cues (Koriat, Lichtenstein, & Fischhoff,
1980; Sniezek, Paese, & Switzer III, 1990; Thiede, Griffin, Wiley,
& Anderson, 2010).
To sum up, the results of this study point to specific metacognitive deficits in on-screen learning that do not appear to reflect
difficulties in information encoding per se. In its attempt to break
new ground in applying a metacognitive framework to uncover
ACKERMAN AND GOLDSMITH
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30
and explain differences in learning from paper and computer
screen, the present research perhaps raises more questions than it
answers. Nevertheless, several potentially important implications
of the findings can be specified: First, they call into question the
common assumption that as long as no new technological skills are
explicitly required, learners can adapt seamlessly into computerized learning environments by applying skills proven to be effective on paper (see also Garland & Noyes, 2004). Second, they raise
new considerations in the development of computerized learning
and testing environments, particularly when reading comprehension is involved. As just one example, when a test requires students
to read a text and then answer questions, the test score is likely to
reflect the effectiveness of metacognitive processes, such as time
allocation and knowledge monitoring, in addition to the specific
object-level knowledge or cognitive ability that is being targeted
(see, e.g., Budescu & Bar-Hillel, 1993; Higham, 2007; Koriat &
Goldsmith, 1998). If so, test scores may differ when the test is
administered on screen versus on paper, and individual differences
in the influence of test media on metacognitive effectiveness may
add unwanted variance to the test scores. Third, because computerized learning environments are already ubiquitous, ways should
be devised to improve the metacognitive skills of screen learners
(cf. Kramarski & Dudai, 2009; Roll, Aleven, McLaren, & Koedinger, 2007). Our approach calls for a special focus of these
attempts on the effectiveness of “online” metacognitive monitoring and control. Fourth, researchers who investigate study processes in general and metacomprehension in particular should pay
attention to the study (or test) media as a potential intervening
variable and avoid the mixing of tasks on screen and on paper as
if these tasks are completely interchangeable.
Finally, in the present study we examined continuous text learning. It should be interesting to compare the effectiveness of metacognitive learning processes with hypertext and/or multimedia
technologies to those of continuous text learning using the MLRP
methodology. Perhaps a more active or sophisticated learning
environment will enhance the effectiveness of study regulation, or
on the contrary, perhaps the increased complexity and cognitive
load will reduce its effectiveness. In addition, one might examine
different types of learning tasks beyond continuous text learning,
such as information collection and integration from multiple
sources on the World Wide Web (see Britt & Gabrys, 2002; Le
Bigot & Rouet, 2007; Stadtler & Bromme, 2007). More generally,
this study highlights the potential utility of the metacognitive
approach and MLRP methodology in identifying and revealing the
source of subjective and objective differences in learning performance between different study tasks and conditions, learning materials, and learner characteristics.
References
Ackerman, R., & Goldsmith, M. (2008a). Control over grain size in
memory reporting — with and without satisficing knowledge. Journal of
Experimental Psychology: Learning, Memory, and Cognition, 34, 1224 –
1245
Ackerman, R., & Goldsmith, M. (2008b). Learning directly from screen?
Oh-no, I must print it! Metacognitive analysis of digitally presented text
learning. In Y. Eshet-Alkalai, A. Caspi, & N. Geri (Eds.), Proceedings
of the Chais conference on instructional technologies research 2008:
Learning in the technological era (Vol. 3, pp. 1–7). Raanana, Israel:
Open University of Israel.
Annand, D. (2008). Learning efficacy and cost-effectiveness of print versus
e-book instructional material in an introductory financial accounting
course. Journal of Interactive Online Learning, 7, 152–164.
Azevedo, R., & Cromley, J. G. (2004). Does training on self-regulated
learning facilitate students’ learning with hypermedia? Journal of Educational Psychology, 96, 523–535.
Baker, J., & Dunlosky, J. (2006). Does momentary accessibility influence
metacomprehension judgments? The influence of study-judgment lags
on accessibility effects. Psychonomic Bulletin & Review, 13, 60 – 65.
Baker, L. (1985). How do we know when we don’t understand? Standards
for evaluating text comprehension. In D. L. Forrest-Pressley & G. E.
Mackinnon & T. G. Waller (Eds.), Metacognition, cognition and human
performance (pp. 155–205). New York: Academic.
Bandura, A. (1989). Regulation of cognitive processes through perceived
self-efficacy. Developmental Psychology, 25, 729 –735.
Benjamin, A. S., Bjork, R. A., & Schwartz, B. L. (1998). The mismeasure
of memory: When retrieval fluency is misleading as a metamnemonic
index. Journal of Experimental Psychology: General, 127, 55– 68.
Benjamin, A. S., & Diaz, M. (2008). Measurement of relative metamnemonic accuracy. In J. Dunlosky & R. A. Bjork (Eds.), Handbook of
memory and metamemory (pp. 73–94). New York: Psychology Press.
Berry, J. M., West, R. L., & Dennehey, D. M. (1989). Reliability and
validity of the memory self-efficacy questionnaire. Developmental Psychology, 25, 701–713.
Bielaczyc, K., Pirolli, P. L., & Brown, A. L. (1995). Training in selfexplanation and self-regulation strategies: Investigating the effects of
knowledge acquisition activities on problem solving. Cognition and
Instruction, 13, 221–252.
Bjork, R. A. (1994). Memory and metamemory considerations in the
training of human beings, Metacognition: Knowing about knowing (pp.
185–205). Cambridge, MA: MIT Press.
Britt, M. A., & Gabrys, G. (2002). Implications of document-level literacy
skills for Web site design. Behavior Research Methods, Instruments, &
Computers, 34, 170.
Brown, A. L., Smiley, S. S., & Lawton, S. Q. C. (1978). The effects of
experience on the selection of suitable retrieval cues for studying texts.
Child Development, 49, 829 – 835.
Budescu, D., & Bar-Hillel, M. (1993). To guess or not to guess: A
decision-theoretic view of formula scoring. Journal of Educational
Measurement, 30, 277–291.
Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning:
A theoretical synthesis. Review of Educational Research, 65, 245–281.
Buxton, B. (2008). The next Google. Nature, 455, 8 –10.
Buzzetto-More, N., Sweat-Guy, R., & Elobaid, M. (2007). Reading in a
digital age: E-books: Are students ready for this learning object? Interdisciplinary Journal of Knowledge and Learning Objects, 3, 239 –250.
Chou, S. W., & Liu, C. H. (2005). Learning effectiveness in a Web-based
virtual learning environment: A learner control perspective. Journal of
Computer Assisted Learning, 21, 65–76.
Chumley-Jones, H. S., Dobbie, A., & Alford, C. L. (2002). Web-based
learning: Sound educational method or hype? A review of the evaluation
literature. Academic Medicine, 77, S86 –S93.
Coiro, J., Knobel, M., Lankshear, C., & Leu, D. J. (Eds.), (2008). Handbook of research on new literacies. Mahwah, NJ: Erlbaum.
Danion, J. M., Gokalsing, E., Robert, P., Massin-Krauss, M., & Bacon, E.
(2001). Defective relationship between subjective experience and behavior in schizophrenia. American Journal of Psychiatry, 158, 2064 –
2066.
Dilevko, J., & Gottlieb, L. (2002). Print sources in an electronic age: A
vital part of the research process for undergraduate students. The Journal
of Academic Librarianship, 28, 381–392.
Dillon, A. (1992). Reading from paper versus screens-a critical review of
the empirical literature. Ergonomics, 35, 1297–1326.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
METACOGNITIVE REGULATION OF TEXT LEARNING
Dillon, A. (1994). Designing usable electronic text: Ergonomic aspects of
human information usage. Philadelphia: Taylor and Francis.
Dillon, A. (2002). Technologies of information: HCI and the digital library.
In J. M. Carroll (Ed.), Human-Computer Interaction in the new millennium (pp. 457– 474). Boston: Addison Wesley/ACM Press.
Dillon, A., McKnight, C., & Richardson, J. (1988). Reading from paper
versus reading from screens. The Computer Journal, 31, 457– 464.
Dillon, A., Richardson, J., & McKnight, C. (1990). The effects of display
size and text splitting on reading lengthy text from screen. Behaviour &
Information Technology, 9, 215–227.
Dufresne, A., & Kobasigawa, A. (1989). Children’s spontaneous allocation
of study time: Differential and sufficient aspects. Journal of Experimental Child Psychology, 47, 274 –296.
Dunlosky, J., & Connor, L. T. (1997). Age differences in the allocation of
study time account for age differences in memory performance. Memory
& Cognition, 25, 691–700.
Dunlosky, J., Serra, M. J., Matvey, G., & Rawson, K. A. (2005). Secondorder judgments about judgments of learning. The Journal of General
Psychology, 132, 335–346.
Dunlosky, J., & Thiede, K. W. (1998). What makes people study more? An
evaluation of factors that affect self-paced study. Acta Psychologica, 98,
37–56.
Eshet-Alkalai, Y. (2004). Digital literacy: A conceptual framework for
survival skills in the digital era. Journal of Educational Multimedia and
Hypermedia, 13, 93–106.
Eshet-Alkalai, Y., & Geri, N. (2007). Does the medium affect the message?
The influence of text representation format on critical thinking. Human
Systems Management, 26, 269 –279.
Garland, K. J., & Noyes, J. M. (2004). CRT monitors: Do they interfere
with learning? Behaviour & Information Technology, 23, 43–52.
Glenberg, A. M., Sanocki, T., Epstein, W., & Morris, C. (1987). Enhancing
calibration of comprehension. Journal of Experimental Psychology:
General, 116, 119 –136.
Glenberg, A. M., Wilkinson, A. C., & Epstein, W. (1982). The illusion of
knowing: Failure in the self-assessment of comprehension. Memory &
Cognition, 10, 597– 602.
Goldsmith, M., & Koriat, A. (2008). The strategic regulation of memory
accuracy and informativeness. In A. Benjamin and B. Ross (Eds.),
Psychology of Learning and Motivation, Vol. 48: Memory use as skilled
cognition (pp. 1– 60). San Diego, CA: Elsevier.
Hacker, D. J. (1998). Self-regulated comprehension during normal reading.
In D. J. Hacker & J. Dunlosky & A. C. Graesser (Eds.), Metacognition
in education theory and practice (pp. 165–192). Mahwah, NJ: Erlbaum.
Higham, P. A. (2007). No Special K! A signal detection framework for the
strategic regulation of memory accuracy. Journal of Experimental Psychology: General, 136, 1–22.
Kintsch, W. (1998). Comprehension: A paradigm for cognition. New
York: Cambridge.
Kong-King, S., & Chin-Chiuan, L. (2000). Effects of screen type, ambient
illumination, and color combination on VDT visual performance and
subjective preference. International Journal of Industrial Ergonomics,
26, 527–536.
Koren, D., Seidman, L. J., Poyurovsky, M., Goldsmith, M., Viksman, P.,
Zichel, S., & Klein, E. (2004). The neuropsychological basis of insight
in first-episode schizophrenia: A pilot metacognitive study. Schizophrenia Research, 70, 195–202.
Koriat, A. (1997). Monitoring one’s own knowledge during study: A
cue-utilization approach to judgments of learning. Journal of Experimental Psychology: General, 126, 349 –370.
Koriat, A., Ackerman, R., Lockl, K., & Schneider, W. (2009). The memorizing effort heuristic in judgments of learning: A developmental perspective. Journal of Experimental Child Psychology, 102, 265–279.
Koriat, A., & Goldsmith, M. (1996). Monitoring and control processes in
31
the strategic regulation of memory accuracy. Psychological Review, 103,
490 –517.
Koriat, A., & Goldsmith, M. (1998). The role of metacognitive processes
in the regulation of memory performance. In G. Mazzoni & T. O. Nelson
(Eds.), Metacognition and cognitive neuropsychology: Monitoring and
control processes (pp. 97–118). Hillsdale, NJ: Erlbaum.
Koriat, A., Lichtenstein, S., & Fischhoff, B. (1980). Reasons for confidence. Journal of Experimental Psychology: Human Learning and Memory, 6, 107–118.
Kornell, N., & Metcalfe, J. (2006). Study efficacy and the region of
proximal learning framework. Learning, Memory, 32, 609 – 622.
Kramarski, B., & Dudai, V. (2009). Group-metacognitive support for
online inquiry in mathematics with differential self-questioning. Journal
of Educational Computing Research, 40, 377– 404.
LaPorte, R. E., & Nath, R. (1976). Role of performance goals in prose
learning. Journal of Educational Psychology, 68, 260 –264.
Le Bigot, L., & Rouet, J. F. (2007). The impact of presentation format, task
assignment, and prior knowledge on students’ comprehension of multiple online documents. Journal of Literacy Research, 39, 445– 470.
Le Ny, J. F., Denhiere, G., & Le Taillanter, D. (1972). Regulation of
study-time and interstimulus similarity in self-paced learning conditions.
Acta Psychologica, 36, 280 –289.
Lichtenstein, S., Fischhoff, B., & Phillips, L. D. (1982). Calibration of
probabilities: The state of the art to 1980. Judgment under uncertainty:
Heuristics and biases (pp. 306-334). New York: Cambridge University
Press.
Lockl, K., & Schneider, W. (2004). The effects of incentives and instructions on children’s allocation of study time. European Journal of Developmental Psychology, 1, 153–169.
Lundeberg, M. A., Fox, P. W., & Punćochaŕ, J. (1994). Highly confident
but wrong: Gender differences and similarities in confidence judgments.
Journal of Educational Psychology, 86, 114 –121.
Macedo-Rouet, M., Rouet, J.-F., Epstein, I., & Fayard, P. (2003). Effects
of online reading on popular science comprehension. Science Communication, 25, 99 –128.
Maki, R. H. (1995). Accuracy of metacomprehension judgments for questions of varying importance levels. American Journal of Psychology,
108, 327–344.
Maki, R. H. (1998a). Predicting performance on text: Delayed versus
immediate predictions and tests. Memory & Cognition, 26, 959 –964.
Maki, R. H. (1998b). Test predictions over text material. In D. J. Hacker
(Ed.), Metacognition in educational theory and practice. The educational psychology series (pp. 117–144). Mahwah, NJ, US: Erlbaum,
Publishers.
Maki, R. H., & Serra, M. (1992). The basis of test predictions for text
material. Journal of Experimental Psychology: Learning, Memory, and
Cognition, 18, 116 –126.
Marmaras, N., Nathanael, D., & Zarboutis, N. (2008). The transition from
CRT to LCD monitors: Effects on monitor placement and possible
consequences in viewing distance and body postures. International
Journal of Industrial Ergonomics, 38, 584 –592.
Masson, M. E. J., & Rotello, C. M. (2009). Sources of bias in the
Goodman-Kruskal gamma coefficient measure of association: Implications for studies of metacognitive processes. Journal of Experimental
Psychology: Learning, Memory, and Cognition, 35, 509 –527.
Mayer, R. E. (2003). The promise of multimedia learning: Using the same
instructional design methods across different media. Learning and Instruction, 13, 125–139.
Mazzoni, G., Cornoldi, C., & Marchitelli, G. (1990). Do memorability
ratings affect study-time allocation? Memory & Cognition, 18, 196 –204.
Menozzi, M., Lang, F., Näpflin, U., Zeller, C., & Krueger, H. (2001). CRT
versus LCD: Effects of refresh rate, display technology and background
luminance in visual performance. Displays, 22, 79 – 85.
Metcalfe, J. (1998). Cognitive optimism: Self-deception or memory-based
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