This study explores the integration of Music Information Retrieval (MIR) techniques into learning analytics to analyze students' background music choices during virtual reality (VR) content creation. Through a mixed-methods approach involving interviews with 16 students and the analysis of background music in 98 VR stories, we examined students' music selection strategies and the emotional and stylistic characteristics of their chosen audio. Findings reveal a preference for calm, low-arousal music aligning with cultural heritage themes. The study demonstrates the potential of MIR in educational contexts, contributing to the emerging field of multimodal learning analytics.

This paper presents the design and implementation of a student-facing Learning Analytics Dashboard (LAD) to support collaborative Virtual Reality (VR) content creation in educational settings. The LAD includes a shared task checklist, class progress statistics, and visualizations of student contributions to promote self-regulated learning (SRL) strategies and group awareness. The effectiveness of the LAD was evaluated through a quasi-experiment in a high school VR creation program with a total of 111 students. Results revealed high adoption rates of the LAD and significant differences in editing behaviours between the control and experimental groups. This study demonstrates the LAD's potentials in fostering iterative improvements and enhancing collaboration in collaborative maker activities.

With the rise of online learning platforms, the need for effective learning analytics (LA) has become critical for teachers. However, the development of traditional LA dashboards often requires technical expertise and a certain level of data literacy, preventing many teachers from integrating LA dashboards effectively and flexibly into their teaching practice. This paper explores the development of a self-service teacher-facing learning analytics dashboard powered by large language models (LLMs), for improving teaching practices. By leveraging LLMs, the self-service system aims to simplify the implementation of data queries and visualizations, allowing teachers to create personalized LA dashboards using natural languages. This study also investigates the capabilities of LLMs in generating charts for LA dashboards and evaluates the effectiveness of the self-service system through usability tests with 15 teachers. Preliminary findings suggest that LLMs demonstrate high capabilities in generating charts for LA dashboards, and the LLM-powered self-service system can effectively address participating teachers’ pedagogical needs for LA. This research contributes to the ongoing research on the intersection of LLMs and education, emphasizing the potential of self-service systems to empower teachers in daily teaching practices.

In this paper, we present an empirical study on collaborative virtual reality (VR) maker activities. A platform called CLEVR was designed to facilitate real-time VR co-creation with group awareness features. We develop an assessment framework for collaborative quality using log-based learning analytics (LA). By conducting time-series analysis on five pairs of participants, we demonstrate the potential of our platform and methodology for the development of LA tools to understand and support collaborative learning.

While current evaluation of maker activities has rarely explored students’ learning processes, the multi-perspective and multi-level nature of collaboration adds complexity to learning processes of collaborative maker activities. In terms of group dynamics as an important indicator of collaboration quality, extant studies have shown the benefits of synchrony between learners’ actions during collaborative learning processes. However, synchrony of learners’ cognitive processes and visual attention in collaborative maker activities remains under-explored. Leveraging the multimodal learning analytics (MMLA) approach, this pilot study examines learners’ synchrony patterns from multiple modalities of data in the collaborative maker activity of virtual reality (VR) content creation. We conducted a user experiment with five pairs of students, and collected and analyzed their electroencephalography (EEG) signals, eye movement and system log data. Results showed that the five pairs of collaborators demonstrated diverse synchrony patterns. We also discovered that, while some groups exhibited synchrony in one modality of data before becoming not synchronized in another modality, other groups started with a lack of synchrony followed by maintaining synchrony. This study is expected to make methodological and practical contributions to MMLA research and assessment of collaborative maker activities.

Listening to music is a common behavior when people study or work. However, effects of music listening on studying are still disputed in previous studies. To explore the associations between music characteristics and learning performance and engagement and to develop a music recommender for studying in naturalistic settings, we conducted a two-month field experiment with 51 undergraduate and graduate students. A mobile application based on the experience sampling method was designed and implemented to ubiquitously collect participants' learning status and music listening traces. Statistical tests and machine learning were adopted respectively for uncovering the associations between music listening on learning and constructing a music recommendation model. Results first indicated that learners' music preferences and several musical features were positively correlated with self-reported learning performance and concentration. Furthermore, machine learning modeling demonstrated promising results for developing a music recommender for studying in naturalistic settings. Findings are expected to contribute to research on learning with background music and learning-oriented music recommendation.

Assessments of digital maker activities increasingly rely on automatically analyzing student-created products and their components, such as their textual output. In particular, recent learning analytics research has proposed incorporating text analytic feedback for facilitating students' virtual reality (VR) content creation, though lacking direct empirical evidence from student-created artefacts. Thus, this study examined the relationships between metrics on text in student-created VR content and their learning performance. VR narration scripts and performance scores were collected from 102 students in a maker-based general education course. Results of statistical testing and text mining show that high and low-performing students demonstrated significant differences in such metrics as word counts, vocabulary sizes, and frequent unigrams and bigrams. This study makes methodological and practical contributions in the domains of maker education and learning analytics.

Due to the popularity of virtual reality (VR) in education settings and the rise of maker education, this paper presents LAVR, a platform for VR content creation with learning analytics functions. We design the platform where students can easily create VR stories through a web interface. A learning analytics dashboard is implemented to provide students with feedback on their progress and the quality of the textual content in their VR stories. The platform also offers learning management features for helping teachers set up classrooms with assignments. While the platform will be employed in a forthcoming general education course, we have conducted a preliminary usability evaluation with 12 students and one teacher, and gathered feedback for further refinements before its official launch. The platform will contribute to integrating learning analytics with maker activities.

Being a promising constructionist pedagogy in recent years, maker education empowers students to take agency of their learning process through constructing both knowledge and real-world physical or digital products and fosters peer interactions for collective innovation. Learning Analytics (LA) excels at generating personalized, fine-grained feedback in near real-time and holds much potential in supporting process-oriented and peer-supported learning activities, including maker activities. In the context of virtual reality (VR) content creation for cultural heritage education, this study qualitatively solicited 27 students’ needs on progress monitoring, reflection, and feedback during their making process. Findings have inspired the prototype design of a student-facing LA dashboard (LAVR). Leveraging multimodal learning analytics (MmLA) such as text and audio analytics to fulfill students’ needs, the prototype has various features and functions including automatic task reminders, content quality detection, and real-time feedback on quality of audio-visual elements. A preliminary evaluation of the prototype with 10 students confirms its potential in supporting students’ self-regulated learning during the making process and for improving the quality of VR content. Implications on LA design for supporting maker education are discussed. Future work is planned to include implementation and evaluation of the dashboard in classrooms.

Through profiling learners’ music usage in everyday learning settings and depicting their learning experience when studying with a music app powered by a large-scale and real-world music library, this study revealed preliminary observations on how background music impacts learning under varying task load, and manifested intriguing patterns of learners’ music usage and music preferences in various task load conditions. Specifically, we piloted a three-day field experiment in students’ everyday learning environment. During the experiment, participants performed learning tasks with music in the background and completed a set of online surveys before and after each learning session. Our results suggested that learners’ self-selected, real-life background music could enhance their learning effectiveness, while the beneficial effect of background music was more apparent when the learning task was less mentally or temporally demanding. Towards a closer look at the characteristics of preferable music pieces under various task load conditions, our findings showed that music preferred by participants under high versus low temporal demand differs in a number of characteristics, including speechiness, acousticness, danceability, and energy. This study further reveals the effects of background music on learning under varying task load levels and provides implications for context-aware background music selection when designing musically enriched learning environments.