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The segmenting effect states that people learn better when multimedia instructions are presented in (meaningful and coherent) learner-paced segments, rather than as continuous units. This meta-analysis contains 56 investigations including 88 pairwise comparisons and reveals a significant segmenting effect with small to medium effects for retention and transfer performance. Segmentation also reduces the overall cognitive load and increases learning time. These four effects are confirmed for a system-paced segmentation. The meta-analysis tests different explanations for the segmenting effect that concern facilitating chunking and structuring due to segmenting the multimedia instruction by the instructional designer, providing more time for processing the instruction and allowing the learners to adapt the presentation pace to their individual needs. Moderation analyses indicate that learners with high prior knowledge benefitted more from segmenting instructional material than learners with no or low prior knowledge in terms of retention performance.

This paper reports on the progress of users through 16 Coursera courses taught by University of Pennsylvania faculty for the first time between June 2012 and July 2013. Using descriptive analyses, this study advances knowledge by considering two definitions of massive open online course (MOOC) users (registrants and starters), comparing two approaches to measuring student progress through a MOOC course (sequential versus user driven), and examining several measures of MOOC outcomes and milestones. The patterns of user progression found in this study may not describe current or future patterns given the continued evolution of MOOCs. Nonetheless, the findings provide a baseline for future studies.

The interactions between levels of learner prior knowledge and effectiveness of different instructional techniques and procedures have been intensively investigated within a cognitive load framework since mid-90s. This line of research has become known as the expertise reversal effect. Apart from their cognitive load theory-based prediction and explanation, patterns of empirical findings on the effect fit well those in studies of Aptitude Treatment Interactions (ATI) that were originally initiated in mid-60s. This paper reviews recent empirical findings associated with the expertise reversal effect, their interpretation within cognitive load theory, relations to ATI studies, implications for the design of learnertailored instructional systems, and some recent experimental attempts of implementing these findings into realistic adaptive learning environments.

Using data from embedded participant-observers and a field experiment at the second largest mobile phone factory in the world, located in China, I theorize and test the implications of transparent organizational design on workers' productivity and organizational performance. Drawing from theory and research on learning and control, I introduce the notion of a transparency paradox, whereby maintaining observability of workers may counterintuitively reduce their performance by inducing those being observed to conceal their activities through codes and other costly means; conversely, creating zones of privacy may, under certain conditions, increase performance. Empirical evidence from the field shows that even a modest increase in group-level privacy sustainably and significantly improves line performance, while qualitative evidence suggests that privacy is important in supporting productive deviance, localized experimentation, distraction avoidance, and continuous improvement. I discuss implications of these results for theory on learning and control and suggest directions for future research.

Cognitive load is a theoretical notion with an increasingly central role in the educational research literature. The basic idea of cognitive load theory is that cognitive capacity in working memory is limited, so that if a learning task requires too much capacity, learning will be hampered. The recommended remedy is to design instructional systems that optimize the use of working memory capacity and avoid cognitive overload. Cognitive load theory has advanced educational research considerably and has been used to explain a large set of experimental findings. This article sets out to explore the open questions and the boundaries of cognitive load theory by identifying a number of problematic conceptual, methodological and application-related issues. It concludes by presenting a research agenda for future studies of cognitive load.

Recent evidence indicates that people's judgments of their own learning are causally related to their study behavior and not epiphenomenal. I argue here that people use these metacognitions in an effort to selectively study material in their own region of proximal learning. First they attempt to eliminate materials that are already well learned. Then they progress successively from studying easier to more difficult materials. Successful implementation of this metacognitively guided strategy enhances learning. The necessary components are, first, that the metacognitions be accurate, and second, that the appropriate choices are implemented for study. With these parts in place, the individual is in position to effectively take control of his or her own learning.

User control and human-AI collaboration are two related directions of research in the modern stream of work on human-centered AI. The field of AI in education was an early pioneer in this area of research, but now it lags behind the work on user control and human-AI collaboration in other areas of AI. This paper attempts to motivate further research on learner control and human-AI collaboration in educational applications of AI by presenting a review of the current work and comparing it with similar work in the field of recommender systems.

The terms metacognition, self-regulation, and self-regulated learning appear frequently in the educational literature and are sometimes used interchangeably. In order to explore the theoretical and empirical boundaries between these three constructs and the perceptions or misperceptions that their broad and often unqualified application may engender, an analysis of their use within contemporary research was undertaken. A PsychInfo database search was conducted and 255 studies were identified for a comprehensive data table. Analysis of these data revealed trends that suggest nesting of the constructs in definition and keyword explication. However, important differences emerged in the measures of these three constructs and in environmental factors such as prompting. Implications for future research are discussed.

This study investigated the role of self-directed learning (SDL) in problem-based learning (PBL) and examined how SDL relates to self-regulated learning (SRL). First, it is explained how SDL is implemented in PBL environments. Similarities between SDL and SRL are highlighted. However, both concepts differ on important aspects. SDL includes an additional premise of giving students a broader role in the selection and evaluation of learning materials. SDL can encompass SRL, but the opposite does not hold. Further, a review of empirical studies on SDL and SRL in PBL was conducted. Results suggested that SDL and SRL are developmental processes, that the \"self\" aspect is crucial, and that PBL can foster SDL. It is concluded that conceptual clarity of what SDL entails and guidance for both teachers and students can help PBL to bring forth self-directed learners.

Research on mobile-assisted language learning (MALL) has revealed that high rates of attrition among users can undermine the potential benefits of this learning method. To explore this issue, we surveyed 3,670 adult MALL users based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and also conducted an in-depth analysis of their historical app usage data. The results of hierarchical k-means cluster analysis and recurrent event survival analysis revealed three major findings. First, three distinct profiles of learners were characterized by different MALL acceptance and engagement experiences. Second, those with greater MALL acceptance displayed more intense, frequent, and durable app usage (behavioral engagement). Lastly, high levels of MALL acceptance were associated with more frequent pauses in app usage but also (a) longer active usage, (b) shorter breaks before returning to the app, and, ultimately, (c) fewer dropouts. We argue that persistence is a multidimensional process involving cyclical phases of engagement, disengagement, dormancy, and reengagement, with each aspect, like intensity, frequency, and duration, building up cumulatively over time. Implications for promoting persistent MALL engagement are discussed.