The Role of Mental Effort in Fostering Self-Regulated Learning with Problem-Solving Tasks

Transcending Belief: Exploring the Impact of Belief in a Just World on Self-Regulated Learning in Chinese Adolescents Using Latent Transitions Analysis

Introduction

While numerous studies have investigated the correlations between Belief in a Just World (BJW) and various variables, research on its stability and changes among different individuals, particularly in China, remains limited.

Methods

This study aims to identify the classification and trajectories of BJW among Chinese adolescents using a person-oriented approach, including latent profile analysis and latent transition analysis. It also examines the impact of social status on BJW and its influence on self-regulated learning. The longitudinal data were collected from 756 Chinese high school students (35.71% male, Mage = 16.00, age range = 14-19).

Results

Three distinct BJW profiles were identified: High-level BJW (~33%), Moderate-level BJW (~51%), and Low-level BJW (~14%). The findings indicate that membership in moderate and high BJW profiles was relatively stable, while the low-level BJW profile showed moderate stability. Higher perceptions of BJW were associated with higher subjective social status. Additionally, students in the High-level BJW profile exhibited the highest levels of self-regulated learning, even after controlling for gender, residence, class cadre, and parental educational levels.

Discussion

These results suggest that subjective social status can enhance Belief in a Just World, which in turn fosters better self-regulated learning skills. The study offers insights into the role of BJW and provides implications for interventions aimed at improving BJW and associated outcomes.

Optimizing self-organized study orders: combining refutations and metacognitive prompts improves the use of interleaved practice

During category learning, students struggle to create an optimal study order: They often study one category at a time (i.e., blocked practice) instead of alternating between different categories (i.e., interleaved practice). Several interventions to improve self-study of categorical learning have been proposed, but these interventions have only been tested in learning tasks where students did not create the study order themselves. Instead, they decided which type of study order to follow. This pre-registered experiment examined whether an intervention that combines refutations and metacognitive prompts can enhance students' engagement in interleaved practice, specifically when they organize the learning materials themselves. Ninety-one undergraduate students were randomized into the intervention and control condition and learned visual categories. Prior to the intervention, students used more blocked practice. After the intervention, the use of interleaved practice significantly increased in both immediate and delayed-transfer tasks. More interleaved practice was associated with better classification performance. Our findings indicate that refutations and metacognitive prompts form a strong intervention that corrects students' erroneous beliefs and increases their engagement in interleaved practice.

It's all metacognitive: The relationship between informal learning and self-regulated learning in the workplace

Informal workplace learning accounts for a large extent of employees' learning. Informal learning activities such as reflection or keeping up-to-date resemble self-regulated learning strategies that indicate the ability to plan, monitor, and regulate one's learning. However, little is known about the relationship between informal learning behaviors and self-regulated learning strategies. Structural equation modeling with data from 248 employees revealed that the informal learning behaviors of reflection, keeping up-to-date, feedback-seeking, and knowledge-sharing are strongly related to the metacognitive self-regulated learning strategies of monitoring and regulation. However, informal learning behaviors lack the deep-processing strategies of elaboration and organization, and the resource strategies of help-seeking and effort regulation. Only innovative behavior is strongly related to effort regulation. These results suggest a potential deficit in employees' strategy use. Employees should consider further resources to increase their learning effectiveness in the workplace. The results are discussed, and practical implications are outlined.

Undergraduate Students' Profiles of Cognitive Load in Augmented Reality-Assisted Science Learning and Their Relation to Science Learning Self-efficacy and Behavior Patterns

Research evidence indicated that a specific type of augmented reality-assisted (AR-assisted) science learning design or support might not suit or be effective for all students because students' cognitive load might differ according to their experiences and individual characteristics. Thus, this study aimed to identify undergraduate students' profiles of cognitive load in AR-assisted science learning and to examine the role of their distinct profiles in self-efficacy together with associated behavior patterns in science learning. After ensuring the validity and reliability of each measure, a latent profile analysis confirmed that 365 Chinese undergraduates carried diverse dimensions of cognitive load simultaneously. The latent profile analysis findings revealed four fundamental profiles: Low Engagement, Immersive, Dabbling, and Organized, characterized as carrying various respective cognitive loads. The multivariate analysis of variance findings revealed different levels of the six AR science learning self-efficacy dimensions across profiles. Low Engagement students displayed the lowest self-efficacy among all dimensions. Organized students recorded better conceptual understanding and higher-order cognitive skills than Dabbling ones. Students with the Immersive profile had the highest science learning self-efficacy. The lag sequential analysis results showed significant differences in behavior patterns among profiles. Among them, profiles with social interaction, test, and reviewing feedback behavior had a significantly higher score for self-efficacy than those patterns mainly based on test learning and resource visits. This finding provides a unified consideration of students' diverse profiles and can inform interventions for effective design of AR-assisted science learning to match appropriate strategies to facilitate the science learning effect.

The screen inferiority depends on test format in reasoning and meta-reasoning tasks

Influential work has confirmed screen inferiority in reading tasks that reading on screen is less productive than reading on paper. Recent researches suggest that poor cognitive performance in screen environments may be primarily due to cognitive defects rather than technological flaws. Although some studies have explored screen inferiority in reasoning tasks from cognitive and metacognitive perspectives, related theories have yet to be enriched. Here, we found that screen inferiority exists in reasoning performance regardless of the test format (multiple-choice VS. open-ended), which may result from shallow processing consistent with the previous findings. However, meta-reasoning monitoring showed screen inferiority only in the multiple-choice test format. Our results indicate that the screens exhibit robust inferiority in reasoning scores, while the influence of the media on meta-reasoning may vary with external triggers. Our research may shed light on how to conduct efficient reasoning in the screen age.

Examining Humans’ Problem-Solving Styles in Technology-Rich Environments Using Log File Data

This study investigated how one’s problem-solving style impacts his/her problem-solving performance in technology-rich environments. Drawing upon experiential learning theory, we extracted two behavioral indicators (i.e., planning duration for problem solving and human–computer interaction frequency) to model problem-solving styles in technology-rich environments. We employed an existing data set in which 7516 participants responded to 14 technology-based tasks of the Programme for the International Assessment of Adult Competencies (PIAAC) 2012. Clustering analyses revealed three problem-solving styles: Acting indicates a preference for active explorations; Reflecting represents a tendency to observe; and Shirking shows an inclination toward scarce tryouts and few observations. Explanatory item response modeling analyses disclosed that individuals with the Acting style outperformed those with the Reflecting or the Shirking style, and this superiority persisted across tasks with different difficulties.

Self-Regulated Learning and Scientific Research Using Artificial Intelligence for Higher Education Systems

Self-regulated learning (SRL) is a very significant ability for students in the learning process, and SRL strategies are used to assist students in learning efficiently in higher education. Many SRL environments face challenges and barriers, including less immediate feedback and guidance, a high level of self-personalization, environment, and resources from teachers or learner designers. Hence, in this paper, the artificial intelligence framework for self-regulated learning (AIF-SRL) overcame the SRL environment's challenges in higher education. Self-regulated students are active participants in their learning and may select from a strategic portfolio and monitor their progress towards the objective. The proposed artificial intelligence-based solution has been used to monitor student behavior through students' feedback and responses. The experimental results show that the proposed AIF-SRL method improves the student's self-evaluation, self-regulation behavior, self-efficacy, learning gain, and self-satisfaction compared to other existing methods.

The Influence of Self-Regulation on Learner's Behavioral Intention to Reuse E-Learning Systems: A Moderated Mediation Model

Learners' behavioral intention to reuse e-learning is of great significance to the implementation and management of e-learning in higher education. This study examined the relationship between self-regulation and behavioral intention to reuse by focusing on the mediating role of study engagement and the moderating role of peer collaboration. Based on a sample of 379 undergraduates from central China, we found that self-regulation positively influences behavioral intention to reuse via study engagement. In addition, moderated path analysis indicated that peer collaboration strengthened the direct effect of self-regulation on study engagement and its indirect effect on behavioral intention to reuse. Theoretical and practical implications of these findings are discussed as well.

Five Strategies for Optimizing Instructional Materials: Instructor- and Learner-Managed Cognitive Load

Researchers of cognitive load theory and the cognitive theory of multimedia learning have identified several strategies to optimize instructional materials. In this review article we focus on five of these strategies or solutions to problematic instructional designs in multimedia learning: (a) the multimedia principle (use visualizations and drawings to complement texts); (b) the split-attention effect or spatial contiguity principle (show texts contiguously or integrated with visualizations); (c) the redundancy effect, alike the coherence principle (remove nonessential learning information); (d) the signaling principle (cue or signal essential learning information); and (e) the transient information effect or segmenting principle (segment or control the pace of animations and videos). Usually, both cognitive theories have investigated solutions that instructors, teachers, and designers should pursue to optimize students’ learning. Here, in a novel approach, we show that these strategies can also be used by learners who want to self-manage their cognitive load and learning process. We provide several examples of both instructor- and learner-managed solutions aligned with these strategies. When assessing which agent, either the instructor or the learner, was most effective, we observed mixed results in the literature. However, the expertise reversal effect may help predict the direction of these effects: novice students may learn better under instructor-managed conditions, whereas more expert students may learn more under learner-managed conditions.

To What Extent Do Situation-Model-Approach Interventions Improve Relative Metacomprehension Accuracy? Meta-Analytic Insights

This meta-analysis investigated the extent to which relative metacomprehension accuracy can be increased by interventions that aim to support learners’ use of situation-model cues as a basis for judging their text comprehension. These interventions were delayed-summary writing, delayed-keywords listing, delayed-diagram completion, self-explaining, concept mapping, rereading, and setting a comprehension-test expectancy. First, the general effectiveness of situation-model-approach interventions was examined. The results revealed that, across 28 effect sizes (comprising a total of 2,236 participants), situation-model-approach interventions exerted a medium positive effect (g = 0.46) on relative metacomprehension accuracy. Second, the interventions were examined individually. The results showed that, with the exception of self-explaining, each intervention had a significant positive effect on relative metacomprehension accuracy. Yet, there was a tendency for setting a comprehension-test expectancy to be particularly effective. A further meta-analysis on comprehension in the selected studies revealed that, overall, the situation-model-approach interventions were also beneficial for directly improving comprehension, albeit the effect was small. Taken together, the findings demonstrate the utility of situation-model-approach interventions for supporting self-regulated learning from texts.

Building Bridges Between Self-Regulation and Cognitive Load—an Invitation for a Broad and Differentiated Attempt

Building bridges between two of the most influential research fields in educational psychology, self-regulation and cognitive load theory, is highly relevant but also challenging. The collection of papers in this special issue reflects this interplay by reviewing the still scarce base of empirical data in an impressively elaborated and profound way. The papers offer different perspectives on how to improve learning by stimulating both activities for self-regulation as well as for reflecting the mental effort which can be used in return for monitoring and regulation. They provide arguments for the two sidedness of the relationship of self-regulation and cognitive load: that cognitive load can cause self-regulation and that self-regulation can cause cognitive load. The common understanding of self-regulation in this issue is very much focused on monitoring and could benefit from a broader view by including the whole cycle of self-regulation and moderating motivational factors like self-efficacy, as proposed in many self-regulation models. The conceptualization of effort, as it is referred to in most of the papers, could also profit from a more differentiated view, which takes into account the origin of required or invested mental effort. Overall, what learners actually decide to do when dealing with self-regulation as well as with cognitive load highly depends on their resources. In an integrative model, the role of potential resources is discussed as a starting point for future research. This discussion invites for an even broader, more individualized, and differentiated view to add to the bridge-building attempts of this impressive collection of research.

Looking at Mental Effort Appraisals through a Metacognitive Lens: Are they Biased?

A central factor in research guided by the Cognitive Load Theory (CLT) is the mental effort people invest in performing a task. Mental effort is commonly assessed by asking people to report their effort throughout performing, learning, or problem-solving tasks. Although this measurement is considered reliable and valid in CLT research, metacognitive research provides robust evidence that self-appraisals of performance are often biased. In this review, we consider the possibility that mental effort appraisals may also be biased. In particular, we review signs for covariations and mismatches between subjective and objective measures of effort. Our review suggests that subjective and most objective effort measures appear reliable and valid when evaluated in isolation, because they discriminate among tasks of varying complexity. However, not much is known about their mutual correspondence—that is, whether subjective measures covariate with objective measures. Moreover, there is evidence that people utilize heuristic cues when appraising their effort, similar to utilization of heuristic cues underlying metacognitive appraisals of performance. These cues are identified by exposing biases—mismatch in effects of cue variations on appraisals and performance. The review concludes with a research agenda in which we suggest applying the well-established methodologies for studying biases in self-appraisals of performance in metacognitive research to investigating effort appraisals. One promising method could be to determine the covariation of effort appraisals and objective effort measures as an indicator of the resolution of effort appraisals.