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.