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This study presents the outcomes of a semi-systematic literature review on the role of learning theory in multimodal learning analytics (MMLA) research. Based on previous systematic literature reviews in MMLA and an additional new search, 35 MMLA works were identified that use theory. The results show that MMLA studies do not always discuss their findings within an established theoretical framework. Most of the theory-driven MMLA studies are positioned in the cognitive and affective domains, and the three most frequently used theories are embodied cognition, cognitive load theory and control-value theory of achievement emotions. Often, the theories are only used to inform the study design, but there is a relationship between the most frequently used theories and the data modalities used to operationalize those theories. Although studies such as these are rare, the findings indicate that MMLA affordances can, indeed, lead to theoretical contributions to learning sciences. In this work, we discuss methods of accelerating theory-driven MMLA research and how this acceleration can extend or even create new theoretical knowledge.

Smart learning environments (SLEs) leverage technological developments to enable effective, engaging, and personalized learning. SLEs rely on sensor and advanced interconnectivity capabilities to infer and reason. A natural set of affordances for SLEs are those of multisensory environments (MSEs). MSEs enable humans to make full use of their senses and equip learning systems with new information, methods of information exchange, and intelligent capabilities. In addition to novel forms of interaction, MSEs also offer novel forms of "learner traces" through multimodal learning analytics (MMLA). This article presents the results of a systematic literature review on how multisensory interactions and the respective analytics can support the use and design of SLEs. The findings from the analysis of 33 papers synthesize and clarify the latest advancements in the intersection of interactions and analytics in MSEs, discuss how those advancements can support SLEs' affordances and uses, and pave the way for improving our understanding of how various interaction modalities can support learning and under what conditions.

Online learning systems should support students preparedness for professional practice by equipping them with the necessary skills while keeping them engaged and active. In that regard, the development of online learning systems that support students' development and engagement with programming is a challenging process. Early career computer science professionals are required not only to understand and master numerous programming concepts but also to efficiently learn how to apply them in different contexts. A prerequisite for an effective and engaging learning process is the existence of adaptive and flexible learning environments that are beneficial for both students and teachers. Students can benefit from personalized content adapted to their individual goals, knowledge, and needs; while teachers can be relieved from the pressure to uniformly and promptly evaluate hundreds of student assignments. This study proposes and puts into practice a method for evaluating learning content difficulty and students' knowledge proficiency utilizing a modified Elo-rating method. The proposed method effectively pairs learning content difficulty with students' proficiency, and creates personalized recommendations based on the generated ratings. The method was implemented in a programming tutoring system and tested with interactive learning content for object oriented-programming. By collecting quantitative and qualitative data from students who used the system for one semester, the findings reveal that the proposed method can generate recommendations that are relevant to students and has the potential to assist teachers in grading students by providing a more holistic understanding of their progress over time.

Background: Problem-solving is a multidimensional and dynamic process that requires and interlinks cognitive, metacognitive, and affective dimensions of learning. However, current approaches practiced in computing education research (CER) are not sufficient to capture information beyond the basic programming process data (i.e., IDE-log data). Therefore, how cognition and affect intertwine and unfold over time in programming problem-solving activities are rarely investigated. Objectives: In this study, we examined how the theory-informed measures from multimodal data that we have selected as proxies for cognitive and affective dimensions of learning, are associated with student performance, and in comparison, to prior-knowledge. Methods: A high-frequency temporal data was collected with a camera, an electroencephalogram, and an eye-tracker from 40 computer science students (bachelor and master studies) in the context of a code-debugging activity. To study the cognitive processes associated with learning we focused on cognitive load theory (CLT) and the human information processing model. In addition, we complemented CLT with the model of affective dynamics in learning to avoid the machine reductionism perspective. Results: Our findings demonstrated that attention, convergent thinking, and frustration were positively correlated with students' successful code-debugging (i.e., performance), and frequently manifested by high performing participants. Cognitive load, memory load, and boredom were negatively correlated with students' performance, and typically manifested by low performing participants. Implications: Extending the context of analysis in reference to student cognitive processes and affective states, affords educators not just to identify lower performers, but also to understand the potential reasons behind their performance, making our method an important contribution in the confluence of CER and the learning technology communities. In addition, the insights extracted from our analyses allow us to discuss potential avenues for improving learning design and the design of interactive learning systems to support the development of students' problem-solving skills.

This study investigates how multimodal user-generated data can be used to reinforce learner reflection, improve teaching practices, and close the learning analytics loop. In particular, the aim of the study is to utilize user gaze and action-based data to examine the role of a mirroring tool (i.e., Exercise View in Eclipse) in orchestrating basic behavioural regulation during debugging. The results demonstrated that students who processed the information presented in the Exercise View and acted upon it, improved their performance and achieved a higher level of success than those who failed to do so. The findings shed light on what constitutes relevant data within a particular learning context in programming using gaze patterns. Moreover, these findings could guide the collection of essential learner-centred analytics for designing usable, modular learning environments based on data-driven approaches.

With the wide expansion of distributed learning environments the way we learn became more diverse than ever. This poses an opportunity to incorporate different data sources of learning traces that can offer broader insights into learner behavior and the intricacies of the learning process. We argue that combining analytics across different e-learning systems can potentially measure the effectiveness of learning designs and maximize learning opportunities in distributed settings. As a step toward this goal, in this study, we considered how to broaden the context of a single learning environment into a learning ecosystem that integrates three separate e-learning systems. We present a cross-platform architecture that captures, integrates, and stores learning-related data from the learning ecosystem. To demonstrate the feasibility and the benefits of cross-platform architecture, we used regression and classification techniques to generate interpretable models with analytics that can be relevant for instructors in understanding learning behavior and sensemaking of the instructional method on learner performance. The results show that combining data across three e-learning systems improve the classification accuracy compared to data from a single learning system by a factor of 5. This article highlights the value of cross-platform learning analytics and presents a springboard for the creation of new cross-system data-driven research practices.

Purpose: Governments and organizations worldwide are concerned over the declining number of young people choosing to study Science, Technology, Engineering and Mathematics (STEM), especially after the age of 16. Research has foregrounded that students with positive attitudes toward science are more likely to find it relevant and aspire to a science career. This study aims to understand the factors shaping students' attitudes as these are pivotal in promoting science learning. Design/methodology/approach: This study uses the framework of science capital to understand what shapes young people's engagement with or resistance to science. The authors conducted four Computational Thinking making-based workshops with 106 children aged 15-16 years, of which 58 filled in a questionnaire and 22 were interviewed. Statistical and content analyses were performed respectively. Findings: The results indicate that children who are more exposed to science-related activities and contexts are more likely to have higher self-efficacy, and that those with higher prior coding experience scored higher in their self-efficacy and science capital. Six themes emerged from the content analysis, highlighting the diverse factors shaping students' attitudes, such as teaching methods, stereotypes and the degree of difficulty encountered while engaging with science in and out of school. Originality/value: By combining qualitative and quantitative methods with the use of science capital, the authors found a number of aspects of the school experience that shape students' attitudes to science learning in and out of school, as well as their science career aspirations.

In the era of digitalization of learning and teaching processes, Educational Data Literacy (EDL) is highly valued and is becoming essential. EDL is conceptualized as the ability to collect, manage, analyse, comprehend, interpret, and act upon educational data in an ethical, meaningful, and critical manner. The professionals in the field of digitally supported education, i.e., Instructional Designers (IDs) and e-Tutors (eTUTs) of online and blended courses, need to be ready to inform their decisions with educational data, and face the upcoming data-related challenges; they need to update and enhance their profiles with relevant competences. This paper proposes a framework for EDL competence profiles of IDs/eTUTs and evaluates the proposal with the participation of worldwide professionals (N = 210) with experience in digitally supported education. The evaluation aims at validating the proposal and assesses: (1) the current EDL-readiness of IDs/eTUTs; and (2) the extent to which the framework captures and describes the essential EDL competences. The findings indicate that professionals are not EDL-competent yet, but the proposed dimensions and related competences are offering a solid approach to support EDL development.