Integrating metacognitive judgments and eye movements using sequential pattern mining to understand processes underlying multimedia learning

Investigating social comparison behaviour in an immersive virtual reality classroom based on eye-movement data

Higher-achieving peers have repeatedly been found to negatively impact students' evaluations of their own academic abilities (i.e., Big-Fish-Little-Pond Effect). Building on social comparison theory, this pattern is assumed to result from students comparing themselves to their classmates; however, based on existing research designs, it remains unclear how exactly students make use of social comparison information in the classroom. To determine the extent to which students (N = 353 sixth graders) actively attend and respond to social comparison information in the form of peers' achievement-related behaviour, we used eye-tracking data from an immersive virtual reality (IVR) classroom. IVR classrooms offer unprecedented opportunities for psychological classroom research as they allow to integrate authentic classroom scenarios with maximum experimental control. In the present study, we experimentally varied virtual classmates' achievement-related behaviour (i.e., their hand-raising in response to the teacher's questions) during instruction, and students' eye and gaze data showed that they actively processed this social comparison information. Students who attended more to social comparison information (as indicated by more frequent and longer gaze durations at peer learners) had less favourable self-evaluations. We discuss implications for the future use of IVR environments to study behaviours in the classroom and beyond.

Robust SSRL analysis framework for intervention strategy construction in CSCL environment

While the importance of socially shared regulatory of learning (SSRL) in computer-supported collaborative learning (CSCL) environments has increasingly been emphasized, a surge of research has been conducted to identify socially shared regulation activities and their transition sequences. However, little research has been carried out on constructing a systematic framework in which significant regulation activities and transition sequences can be mined automatically with high reliability. Moreover, though efforts have been made, the current SSRL analysis neither serves the construction of downstream teaching intervention strategy nor explores how SSRL analysis results can be utilized conversely for refining the intervention strategy. Based on advanced machine learning techniques, this work proposes a robust framework on SSRL analysis, aiming to find the optimal teaching intervention strategy to improve learners' performance in CSCL by analyzing the SSRL process. In particular, our framework can automatically identify significant SSRL regulation activities along with high-contribution activity transition sequences. The proposed Ensemble Learning-based classification model with four distilled additional regulation activities can ensure the high reliability of our framework. The framework serves to construct a downstream teaching intervention strategy, while the strategy is updated and verified based on empirical and experimental statistical results within five rounds of iterative experiments. Extensive theoretical analysis and experimental results both confirm the effectiveness of our framework. Meanwhile, the attempt to leverage advanced machine learning algorithms to enhance SSRL analysis in this work can provide a nontrivial contribution to the literature.

Toward Better Structure and Constraint to Mine Negative Sequential Patterns

Nonoccurring behavior (NOB) studies have attracted the growing attention of scholars as a crucial part of behavioral science. As an effective method to discover both NOB and occurring behaviors (OB), negative sequential pattern (NSP) mining is successfully used in analyzing medical treatment and abnormal behavior patterns. At this time, NSP mining is still an active and challenging research domain. Most of the algorithms are inefficient in practice. Briefly, the key weaknesses of NSP mining are: 1) an inefficient positive sequential pattern (PSP) mining process, 2) a strict constraint of negative containment, and 3) the lack of an effective Negative Sequential Candidate (NSC) generation method. To address these weaknesses, we propose a highly efficient algorithm with improved techniques, named sc-NSP, to mine NSP efficiently. We first propose an improved PrefixSpan algorithm in the PSP mining process, which connects to a bitmap storage structure instead of the original structure. Second, sc-NSP loosens the frequency constraint and exploits the NSC generation method of positive and negative sequential patterns mining (PNSP) (a classic NSP mining method). Furthermore, a novel pruning strategy is designed to reduce the computational complexity of sc-NSP. Finally, sc-NSP obtains the support of NSC by using the most efficient bitwise-based calculation operation. Theoretical analyses show that sc-NSP performs particularly well on data sets with a large number of elements and items in sequence. Comparison and extensive experiments along with case studies on health data show that sc-NSP is 10 times more efficient than other state-of-the-art methods, and the number of NSPs obtained is 5 times greater than other methods.

Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques

In recent decades, the use of technological resources such as the eye tracking methodology is providing cognitive researchers with important tools to better understand the learning process. However, the interpretation of the metrics requires the use of supervised and unsupervised learning techniques. The main goal of this study was to analyse the results obtained with the eye tracking methodology by applying statistical tests and supervised and unsupervised machine learning techniques, and to contrast the effectiveness of each one. The parameters of fixations, saccades, blinks and scan path, and the results in a puzzle task were found. The statistical study concluded that no significant differences were found between participants in solving the crossword puzzle task; significant differences were only detected in the parameters saccade amplitude minimum and saccade velocity minimum. On the other hand, this study, with supervised machine learning techniques, provided possible features for analysis, some of them different from those used in the statistical study. Regarding the clustering techniques, a good fit was found between the algorithms used (k-means ++, fuzzy k-means and DBSCAN). These algorithms provided the learning profile of the participants in three types (students over 50 years old; and students and teachers under 50 years of age). Therefore, the use of both types of data analysis is considered complementary.

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.

What Can Eye Movements Tell us about Visual Perception Processes in Classroom Contexts? Commentary on a Special Issue

In this commentary to the Special Issue of Educational Psychology Review on visual perceptual processes, I tie the empirical studies reported in the issue with previous research in other domains to offer some points to be considered in future studies. First, I will point out to issues related to the operationalization of the theoretical constructs. The empirical papers in this Special Issue use eye tracking to study students’ engagement, teachers’ expertise, and student-teacher interaction. However, it is not always clear how the observed eye movement patterns reflect these theoretical concepts and the underlying psychological processes. Second, I will reflect on the analyses of the eye movement data presented in the papers. The main advantage of the methodology is that it can provide detailed information about the time-course of processing, and to fully engage its potential, it should be complemented with adequate statistical methods. In my view, the papers in this Special Issue provide valuable novel information about the complex processes underlying learning in variable contexts, and offer an excellent starting point for future research.