Search

Multiple-choice practice tests are beneficial for learning, and students encounter multiple-choice questions regularly. How do students regulate their use of multiple-choice practice testing? And, how effective is students' use of multiple-choice practice testing? In the current experiments, undergraduate participants practiced German-English word pairs. Students started with an initial study trial for each pair. Then, they had the options to restudy an item, take a practice test, or remove it from further practice. For comparison to students' use of multiple-choice practice questions, we included a second self-regulated group that had access to cued-recall practice questions. Participants chose to complete multiple-choice questions until they correctly answered each item about one time during practice, similar to students' use of cued-recall questions. We also included experimenter-controlled groups in which participants completed practice tests until they reached a higher number of correct answers during practice. As compared to the experimenter-controlled groups, participants who regulated their use of multiple-choice questions scored lower on final tests but also spent less time practicing items. Thus, when considering final test performance in relation to time spent practicing, students' choices to use multiple-choice practice questions to about one correct answer per item was comparatively effective.

Prior research has found gender differences in spatial tasks in which men perform better, and are more confident, than women. Do gender differences also occur in people's confidence as they perform number-line estimation, a common spatial-numeric task predictive of math achievement? To investigate this question, we analyzed outcomes from six studies (N = 758 girls/women and boys/men with over 20,000 observations; grades 1-5 and adults) that involved a similar method: Participants estimated where a provided number (e.g., ¾, 37) was located on a bounded number line (e.g., 0-1; 0-100), then judged their confidence in that estimate. Boys/men were more precise (g = 0.52) and more confident (g = 0.30) in their estimates than were girls/women. Linear mixed model analyses of the trial-level data revealed that girls'/women's estimates had about 31% more error than did boys'/men's estimates, and even when controlling for precision, girls/women were about 7% less confident in their estimates than were boys/men. These outcomes should encourage researchers to consider gender differences for studies on math cognition and provide pathways for future research to address potential mechanisms underlying the present gender gaps. [This article was published in "Metacognition and Learning" (EJ1289459).]

We investigated whether three interventions -- studying incorrect worked examples, studying correct worked examples, or receiving feedback -- improved children's 0-1,000 (Experiment 1) and adults' 1 thousand--1 billion (Experiment 2) number-line estimation precision relative to a no intervention control group. At pretest, participants estimated numbers on number lines (without feedback or examples) and judged their confidence in each estimate. They were then assigned to an intervention group, completed intervention trials, and then estimated numbers and judged their confidence at posttest. Children's and adults' confidence judgments did not increase from pretest to posttest. However, consistent with prior research, feedback improved children's and adults' estimation precision from pretest-to-posttest. Importantly, studying correct worked examples also improved precision, but studying incorrect worked examples did not. Although not all children benefited from training, those who did were able to transfer their knowledge to a number-categorization task. Adults in the feedback group were also able to transfer their learning. These outcomes highlight benefits of studying worked examples for improving children's and adults' estimation precision, but to obtain benefits, corrective feedback must be included.

Metacognition is awareness and control of thinking for learning. Strong metacognitive skills have the power to impact student learning and performance. While metacognition can develop over time with practice, many students struggle to meaningfully engage in metacognitive processes. In an evidence-based teaching guide associated with this paper (https://lse.ascb.org/evidence-based-teaching-guides/student-metacognition), we outline the reasons metacognition is critical for learning and summarize relevant research on this topic. We focus on three main areas in which faculty can foster students' metacognition: supporting student learning strategies (i.e., study skills), encouraging monitoring and control of learning, and promoting social metacognition during group work. We distill insights from key papers into general recommendations for instruction, as well as a special list of four recommendations that instructors can implement in any course. We encourage both instructors and researchers to target metacognition to help students improve their learning and performance.

Prior research has investigated whether learners spontaneously adapt their encoding strategies in anticipation of particular test formats (i.e., the "encoding-strategy adaptation hypothesis"; Finley & Benjamin, 2012). However, the strongest evidence supporting this hypothesis is confounded with test experience (as argued by Cho & Neely, 2017). When learners gain equal experience with each test format, do they adapt their encoding strategy use? Across 3 experiments, participants studied lists of cue-target word pairs and after each list completed either a cued-recall test (recall targets given cues) or a free-recall test (recall targets only). Participants received equal experience with each test format. On a final critical test, participants either received a test in a format they expected or one that violated their expectations. On this critical test, participants who received a test they expected outperformed those who did not, and this was true for both cued and free recall. Also, a manipulation of cue-target associative strength had a greater effect on cued-recall tests than free-recall tests (Experiment 1), whereas a manipulation of target-target associative strength had a greater influence on free-recall tests than cued-recall tests (Experiments 2 and 3). These findings, along with divergent patterns of self-reported strategy use for the 2 anticipated test formats, support the encoding-strategy adaptation hypothesis. In particular, learners tend to use more cue-target associative strategies when expecting a cued-recall test, and more target-focused strategies when expecting a free-recall test.

Learners can study foreign language-English vocabulary (e.g., denken – to think) both receptively and productively. Receptive learning involves being cued with a foreign language word (e.g., denken) and trying to translate it (i.e., to think). Productive learning involves being cued with an English word (e.g., to think) and trying to produce the translation. When students use retrieval practice to learn foreign-language translations in one direction (e.g., receptively) until they correctly recall the translation, do they demonstrate transfer in the other direction (i.e., productively)? Across three experiments, we answered this question by manipulating the order of learning schedule (reception first followed by production or vice versa). For a given schedule, participants continued to practice retrieving translations (with feedback) using the dropout method until they correctly recalled each translation three times; they then proceeded to practice the pairs in the opposite direction until they correctly recalled each translation three times. Across all experiments, transfer was partial (learning in one direction did not entirely eliminate the need to practice in the other), but transfer did occur regardless of which schedule students used first during practice. [ABSTRACT FROM AUTHOR]

Almost anything worth doing takes effort, so it is no surprise that effort has played such a central role in how researchers, theoreticians, instructors, and even students think about student learning and achievement. In this special issue, the authors of the target articles explore the importance of effort to students' self-regulated learning within multiple domains. To further support research progress on effort, we distinguish between objective effort as a direct causal agent of learning gains and effort as a student perception. We argue that understanding effort as a student perception shows promise for discovering ways to improve self-regulated learning and student achievement. In developing these arguments, we consider the contribution of the target articles to five themes relevant to metacognitively driven self-regulated learning, with the aim of fostering progressive research programs aimed at revealing the potential roles of effort in student achievement.

Successive relearning involves practicing to-be-learned content until a designated level of mastery is achieved in each of multiple practice sessions. As compared with practicing the content to the same criterion in a single session, successive relearning has been shown to dramatically boost students' retention of simple verbal materials. Does the documented potency of successive relearning extend to the learning of mathematical procedures? Across three experiments, 431 college students read instructions about how to solve four types of probability problems and were then presented with isomorphic practice problems until they correctly solved three problems of each type. In the "successive relearning" group, students engaged in practice until one problem of each type was correctly solved in each of three different practice sessions. In the "single-session" group, students engaged in practice until three problems of each type were correctly solved in a single practice session. Both groups completed a final test involving novel problems 1 week after the end of practice. When an effect size was estimated across all experiments, final test performance showed a significant but only small advantage of successive relearning over single-session learning (pooled d = 0.28, 95% CI = 0.08, 0.49). Secondary analyses revealed that correctly solving a problem did not significantly boost the likelihood of subsequent success, which also could explain the relatively low level of test performance for both groups. These outcomes identify a potential boundary condition for the benefits of using successive relearning to enhance student achievement when learning mathematical procedures.

"Successive relearning" combines two effective learning techniques (retrieval practice and spaced practice) and involves practicing retrieval until some level of mastery has been reached (i.e., at least one correct retrieval attempt) in each of multiple sessions. Several laboratory studies have demonstrated the promise of successive relearning for enhancing student learning, but attempts to evaluate its effectiveness in authentic educational contexts are limited. In the current research, we implemented successive relearning in a difficult biopsychology course to examine its effectiveness at enhancing student learning on a high-stakes exam. Students successively relearned some course content via a virtual flashcard program, whereas other course content was learned with business-as-usual activities. In two experiments, successive relearning boosted students' learning of course content by at least 10% (with ds ranging from 0.54 to 1.10). These findings suggest that implementing successive relearning in courses can be an effective method of promoting student achievement.

Many students use laptops to take notes in classes, but does using them impact later test performance? In a high-profile investigation comparing note-taking writing on paper versus typing on a laptop keyboard, Mueller and Oppenheimer ("Psychological Science," 25, 1159-1168, 2014) concluded that taking notes by longhand is superior. We conducted a direct replication of Mueller and Oppenheimer (2014) and extended their work by including groups who took notes using eWriters and who did not take notes. Some trends suggested longhand superiority; however, performance did not consistently differ between any groups (experiments 1 and 2), including a group who did not take notes (experiment 2). Group differences were further decreased after students studied their notes (experiment 2). A meta-analysis (combining direct replications) of test performance revealed small (nonsignificant) effects favoring longhand. Based on the present outcomes and other available evidence, concluding which method is superior for improving the functions of note-taking seems premature.

This study investigated the efficacy of the Fluency Development Lesson (FDL) in improving reading achievement in primary grade struggling readers. 30 readers, enrolled in a summer reading clinic, participated in daily 40-min mini-reading lessons across 5 weeks. During the fluency lessons, readers practiced and developed their literacy skills through participation in repeated readings, word work, rehearsal, and performance. Assessments measured comprehension, reading fluency, including word recognition accuracy, reading speed, and prosody. Control students also took the pre- and post-intervention tests; their performance did not change with repeated testing. The Fluency Development Lesson students showed significant gains on all measures.

Background: Informal science activities are critical for supporting long-term learning in STEM fields. However, little is known about the kinds of activities children and their families engage in outside of formal settings and how such activities foster long-term STEM engagement. One gap in the literature is the lack of data that document self-designated STEM activities and measure their impact on later engagement with learning opportunities that are distributed over time and contexts (i.e., the informal learning ecology). One reason for this gap is that there has been little measurement during the events, because using only a few measures (which can be completed briefly) may reduce psychometric validity. We developed an instrument, the STEMwhere app, to measure four informal science learning supports (interest, engagement, identity, and goal setting), across the informal learning ecology. For a period of 2 months, 26 children ages 7-14 used the app to check-in during STEM activities and answer eight questions about each activity. Results: The results demonstrated that most STEM activities occurred in the home, often consisted of hands-on activities, suggesting that the family home provides more opportunity for engagement than other locations. Child interest and engagement ratings were high in all settings and activities suggesting that high situational interest was relatively common during these activities. Further, user ratings suggested relations between different learning supports. For example, increases in interest were related to increases in subsequent engagement and "fun" goals, while increases in engagement were related to increases in learning goals. By collecting participant-generated check-ins, we identified periods of increasing activity and their likely triggers, which is a novel measure we refer to as topical runs. We operationally defined a run as a pattern of check-ins that were unlikely to occur by chance and shared a topic or location. Conclusions: Our results serve as both a proof-of-concept for a novel tool for measuring informal STEM activity in the wild that provides data consistent with existing measures and provide novel findings that contribute to our understanding of where and how informal science activity occurs.

We investigated students' knowledge and beliefs about the impact of using three-dimensional (3D) multimedia presentations. Students listened to a lecture about the ventricular system, which was presented alone (Experiment 1 only) or with a 3D or a 2D video illustrating the system. Afterwards, students judged how well they would perform on a criterion test. In Experiment 1, students judged that the 3D presentation would be superior to listening to the lecture alone (d = 0.81). Mean judgments were higher for the 3D than 2D presentation (d = 0.24), but this difference was not significant, so we estimated the effect size again. In Experiment 2, judgments were significantly higher after the 3D than 2D presentations (d = 0.40). Test performance was not significantly greater after the 3D than 2D presentations. A survey study again revealed that students believe 3D presentations are superior, and most students reported preferring them to 2D presentations.

Before the "big test" did you use the following study strategies: highlighting, rereading, and cramming? As students many of us probably did, yet research shows that while these three strategies are commonly used, they have been ineffective in retaining information. Learning strategies have been discussed in almost every textbook on educational psychology, so many teachers likely have been introduced to at least some of them, but, given the demands of day-to-day teaching, teachers do not always have time to figure out which strategies are best to use. Dunlosky reports that in his years of research he has discovered learning strategies that have proved successful and should be part of the "Student Toolbox" to boost learning. This article presents ten learning strategies for students and then expands on their usage. They are: (1) Practice testing: self-testing or taking practice tests on to-be-learned material; (2) Distributed practice: implementing a schedule of practice that spreads out study activities over time; (3) Interleaved practice: implementing a schedule of practice that mixes different kinds of problems, or a schedule of study that mixes different kinds of material, within a single study session; (4) Elaborative interrogation: generating an explanation for why an explicitly stated fact or concept is true; (5) Self-explanation: explaining how new information is related to known information, or explaining steps taken during problem solving; (6) Rereading: restudying text material again after an initial reading; (7) Highlighting and underlining: marking potentially important portions of to-be-learned materials while reading.; (8) Summarization: writing summaries (of various lengths) of to-be-learned texts; (9) Keyword mnemonic: using keywords and mental imagery to associate verbal materials; and (10) Imagery for text: attempting to form mental images of text materials while reading or listening. Using learning strategies can increase student understanding and achievement, but are only effective if students are motivated to use them correctly. Teaching students how to guide their learning of content using effective strategies will allow them to successfully learn throughout their lifetime.

Over the past decades, memory training interventions have been developed in an attempt to stabilize or enhance memory functioning in aging. Only recently has attention been paid to individual differences in training gains and consequently to predictors of such gains. The aim of the present study was to identify which specific cognitive mechanisms/processes or components of the intervention were responsible for the desired change and which individuals were more responsive to memory strategic training. Eighty-one older adults (aged 55 to 82) were involved in a four-session strategy-adaptation training based on a learner-oriented approach that has previously been found to be effective in improving memory performance in practiced and untrained tasks. Results showed that baseline performance in memory tasks predicted the gains in the practiced task. Baseline performance in memory tasks and other cognitive variables, such as working memory, processing speed, and verbal knowledge predicted transfer effects. Interestingly, we found that the magnitude of training gain on the associative memory practiced task predicted the gains in the transfer tasks, suggesting those who best implemented the targeted strategies during training realized greater transfer to other tasks. Our study shows that older adults with larger cognitive resources will benefit more from interventions focused on the generalization via active processes.

This study set out to examine two important aspects of the use of eWriters by early elementary students. First, it explored the impact of eWriters on literacy motivation and self efficacy of students in Pre-Kindergarten, Kindergarten, and First grade. Second, it explored if and how the technology implementation would affect parent and teacher relationships as measured by interrater reliability. Results demonstrated the use of eWriters in early elementary statistically significantly increased student literacy motivation and self-efficacy. Results also showed a statistically significant increase in interrater reliability from pre-test to post-test between parents and teachers. Parents and teachers were more likely to agree on a child's motivation for learning and perception of abilities (self-efficacy) after the child's use of eWriters at both home and school. These finding provide additional support for the notion that innovative technologies can provide unique opportunities to positively enhance literacy motivation, self-efficacy, and real-virtual-relationships in home and school settings between parents and teachers.

Students often are overconfident when they predict their performance on classroom examinations, and their accuracy often does not improve across exams. One contributor to overconfidence may be that students did not have enough experience, and another is that students may under-use their knowledge of prior exam performance to predict performance on their upcoming exams. To evaluate the former, we examined student prediction accuracy across 13 exams in an introductory course on educational psychology. For the latter, we computed measures that estimate the extent to which students "use" the prior exam score when predicting performance and whether students "should" use the prior exam scores. Several outcomes are noteworthy. First, students were overconfident, and contrary to expectations, this overconfidence did not decline across exams. Second, students' prior exam scores were not related to subsequent predictions, even though prior exam performance showed little bias with respect to predicting future performance. Thus, students appear to under-use prior performance despite its utility for improving prediction accuracy about future exam performance.

When correcting a common misconception, it seems likely that for corrective feedback to be effective, it needs to be believed. In 2 experiments, we assessed how participants' belief in the validity of corrective feedback regarding individual misconceptions influenced knowledge revision. After responding about the validity of a set of misconceptions, participants received either a refutation alone (feedback that they were correct or incorrect) or a refutation accompanied by a supporting explanation, and then rated their belief in the corrective feedback. One week later, participants once again responded about the validity of the misconceptions. Across both experiments, participants corrected their misconceptions more often when they believed the corrective feedback. In addition, participants corrected their misconceptions more often when they had earlier received a refutation with a supporting explanation than when they had received the refutation only. This benefit of supportive explanations on knowledge revision was mediated by belief in the feedback, suggesting that explanations enhance the effectiveness of a correction by increasing belief in the feedback. These findings imply that successful correction of common misconceptions is likely enhanced by techniques that increase people's belief in the validity of the corrective feedback.

Accurate monitoring and control are essential for effective self-regulated learning. These metacognitive abilities may be particularly important for developing math skills, such as when children are deciding whether a math task is difficult or whether they made a mistake on a particular item. The present experiments investigate children's ability to monitor and control their math performance. Experiment 1 assessed task- and item-level monitoring while children performed a number line estimation task. Children in 1st, 2nd, and 4th grade (N = 59) estimated the location of numbers on small- and large-scale number lines and judged their confidence in each estimate. Consistent with their performance, children were more confident in their small-scale estimates than their large-scale estimates. Experiments 2 (N = 54) and 3 (N = 85) replicated this finding in new samples of 1st, 2nd, and 4th graders and assessed task- and item-level control. When asked which estimates they wanted the experimenter to evaluate for a reward, children tended to select estimates associated with lower error and higher confidence. Thus, children can accurately monitor their performance during number line estimation and use their monitoring to control their subsequent performance.

Declarative concepts (i.e., key terms and corresponding definitions for abstract concepts) represent foundational knowledge that students learn in many content domains. Thus, investigating techniques to enhance concept learning is of critical importance. Various theoretical accounts support the expectation that example generation will serve this purpose, but few studies have examined the efficacy of this technique. We conducted three experiments involving 487 undergraduates to investigate the effects of example generation on concept learning and examined factors that may moderate its effectiveness. Students read a short text that introduced eight concepts. Some students were then prompted to generate concrete examples of each concept followed by definition restudy, whereas others only restudied definitions for the same amount of time. Two days later, students completed final tests involving example generation and definition cued recall. Meta-analytic outcomes indicated that example generation yields moderate improvements in learning of declarative concepts, relative to restudy only. Each experiment also included additional groups to investigate potential moderators. Example generation tended to be more effective with spaced versus massed restudy. Despite strong correlations between the quality of examples generated during practice and final test performance, experimental manipulations that improved example quality did not improve learning. In sum, the current work establishes that example generation enhances concept learning and provides an important foundation for further investigating factors that moderate its benefits to learning.

The target articles explore a common hypothesis pertaining to whether perceptually degrading materials will improve reasoning, memory, and metamemory. Outcomes are mixed, yet some evidence was garnered in support of a version of the disfluency hypothesis that includes moderators, and along with evidence from prior research, researchers will likely continue to explore the impact of disfluency on reasoning and learning. Toward this end, evidence and discussion from the target articles also suggest recommendations--both explicitly and implicitly--about how to explore this effect: (a) treat disfluency as a hypothesis to be tested and evaluate the disfluency hypothesis against alternatives; (b) pursue multiple replications of any disfluency effects; (c) attempt to measure differences in processing fluency across conditions; and (d) resist labeling manipulated variables with theoretically-laden terms. We expand on these recommendations in the present commentary.