Temporal variation in children's self-regulated hypermedia learning

Investigating learning processes through analysis of navigation behavior using log files

The empirical study investigates what log files and process mining can contribute to promoting successful learning. We want to show how monitoring and evaluation of learning processes can be implemented in the educational life by analyzing log files and navigation behavior. Thus, we questioned to what extent log file analyses and process mining can predict learning outcomes. This work aims to provide support for learners and instructors regarding efficient learning with computer-based learning environments (CBLEs). We evaluated log file and questionnaire data from students (N = 58) who used a CBLE for two weeks. Results show a significant learning increase after studying with the CBLE with a very high effect size (p < .001, g = 1.71). A cluster analysis revealed two groups with significantly different learning outcomes accompanied by different navigation patterns. The time spent on learning-relevant pages and the interactivity with a CBLE are meaningful indicators for Recall and Transfer performance. Our results show that navigation behaviors indicate both beneficial and detrimental learning processes. Moreover, we could demonstrate that navigation behaviors impact the learning outcome. We present an easy-to-use approach for learners as well as instructors to promote successful learning by tracking the duration spent in a CBLE and the interactivity.

Temporal Assessment of Self-Regulated Learning by Mining Students' Think-Aloud Protocols

It has been widely theorized and empirically proven that self-regulated learning (SRL) is related to more desired learning outcomes, e.g., higher performance in transfer tests. Research has shifted to understanding the role of SRL during learning, such as the strategies and learning activities, learners employ and engage in the different SRL phases, which contribute to learning achievement. From a methodological perspective, measuring SRL using think-aloud data has been shown to be more insightful than self-report surveys as it helps better in determining the link between SRL activities and learning achievements. Educational process mining on the basis of think-aloud data enables a deeper understanding and more fine-grained analyses of SRL processes. Although students’ SRL is highly contextualized, there are consistent findings of the link between SRL activities and learning outcomes pointing to some consistency of the processes that support learning. However, past studies have utilized differing approaches which make generalization of findings between studies investigating the unfolding of SRL processes during learning a challenge. In the present study with 29 university students, we measured SRL via concurrent think-aloud protocols in a pre-post design using a similar approach from a previous study in an online learning environment during a 45-min learning session, where students learned about three topics and wrote an essay. Results revealed significant learning gain and replication of links between SRL activities and transfer performance, similar to past research. Additionally, temporal structures of successful and less successful students indicated meaningful differences associated with both theoretical assumptions and past research findings. In conclusion, extending prior research by exploring SRL patterns in an online learning setting provides insights to the replicability of previous findings from online learning settings and new findings show that it is important not only to focus on the repertoire of SRL strategies but also on how and when they are used.

Multimodal Data Fusion in Learning Analytics: A Systematic Review

Multimodal learning analytics (MMLA), which has become increasingly popular, can help provide an accurate understanding of learning processes. However, it is still unclear how multimodal data is integrated into MMLA. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper systematically surveys 346 articles on MMLA published during the past three years. For this purpose, we first present a conceptual model for reviewing these articles from three dimensions: data types, learning indicators, and data fusion. Based on this model, we then answer the following questions: 1. What types of data and learning indicators are used in MMLA, together with their relationships; and 2. What are the classifications of the data fusion methods in MMLA. Finally, we point out the key stages in data fusion and the future research direction in MMLA. Our main findings from this review are (a) The data in MMLA are classified into digital data, physical data, physiological data, psychometric data, and environment data; (b) The learning indicators are behavior, cognition, emotion, collaboration, and engagement; (c) The relationships between multimodal data and learning indicators are one-to-one, one-to-any, and many-to-one. The complex relationships between multimodal data and learning indicators are the key for data fusion; (d) The main data fusion methods in MMLA are many-to-one, many-to-many and multiple validations among multimodal data; and (e) Multimodal data fusion can be characterized by the multimodality of data, multi-dimension of indicators, and diversity of methods.

Young Learners' Regulation of Practice Behavior in Adaptive Learning Technologies

Although research indicates positive effects of Adaptive Learning Technologies (ALTs) on learning, we know little about young learners’ regulation intentions in this context. Learners’ intentions and self-evaluation determine the signals they deduce to drive self-regulated learning. This study had a twofold approach as it investigated the effect of feed-up and feed-forward reports on practice behavior and learning and explored learners’ self-evaluation of goal-attainment, performance and accuracy. In the experimental condition, learners described their goals and self-evaluated their progress in feed-up and forward reports. We found no conclusive effects of the feed-up and forward reports on learners’ regulation of practice behavior and learning. Furthermore, results indicated that young learners’ self-evaluations of goal attainment and performance were biased. Contrary to other research, we found learners both over- and underestimated performance which was strongly associated with over- or underestimation of goal attainment. Hence the signals learners used to drive regulation were often incorrect, tending to induce over- or under-practicing. Similarly, we found a bias in self-evaluation of accuracy and accuracy attainment. Learners over- or underestimated their accuracy, which was associated with over- or underestimation of accuracy attainment, which may in turn have affected effort regulation. We concluded that goal setting and self-evaluation in feed-up and forward reports was not enough to deduce valid regulatory signals. Our results indicate that young learners needed performance feedback to support correct self-evaluation and to correctly drive regulatory actions in ATLs.