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As universities around the world have begun to use learning management systems (LMSs), more learning data have become available to gain deeper insights into students' learning processes and make data-driven decisions to improve student learning. With the availability of rich data extracted from the LMS, researchers have turned much of their attention to learning analytics (LA) applications using educational data mining techniques. Numerous LA models have been proposed to predict student achievement in university courses. To design predictive LA models, researchers often follow a data-driven approach that prioritizes prediction accuracy while sacrificing theoretical links to learning theory and its pedagogical implications. In this study, we argue that instead of complex variables (e.g., event logs, clickstream data, timestamps of learning activities), data extracted from online formative assessments should be the starting point for building predictive LA models. Using the LMS data from multiple offerings of an asynchronous undergraduate course, we analysed the utility of online formative assessments in predicting students' final course performance. Our findings showed that the features extracted from online formative assessments (e.g., completion, timestamps and scores) served as strong and significant predictors of students' final course performance. Scores from online formative assessments were consistently the strongest predictor of student performance across the three sections of the course. The number of clicks in the LMS and the time difference between first access and due dates of formative assessments were also significant predictors. Overall, our findings emphasize the need for online formative assessments to build predictive LA models informed by theory and learning design.

Feedback is a crucial component of student learning. As advancements in technology have enabled the adoption of digital learning environments with assessment capabilities, the frequency, delivery format, and timeliness of feedback derived from educational assessments have also changed progressively. Advanced technologies powered by Artificial Intelligence (AI) enable teachers to generate different types of feedback supporting student learning. Despite the rapid uptake of digital technologies in education, previous studies on educational feedback primarily focused on the theoretical underpinnings of feedback practices, which are limited in terms of their coverage of AI-based technologies. This paper aims to inform both researchers and practitioners about the present and future of AI applications in feedback practices, identify and organize potential areas for the use of AI for feedback purposes, and establish venues for AI research and practice in educational feedback. Furthermore, the role of the three branches of AI (i.e., natural language processing, educational data mining, and learning analytics) in feedback practices and potential areas for their future development are discussed.

In higher education, the cultivation of research competency is pivotal for students' critical thinking development and their subsequent transition into the professional workforce. While statistics plays a fundamental role in supporting the completion of a research project, it is often perceived as challenging, particularly by students in majors outside mathematics or statistics. The connection between students' statistical proficiency and their research competency remains unexplored despite its significance. To address this gap, we utilize the supervised machine learning approach to predict students' research competency as represented by their performance in a research methods class, with predictors of students' proficiency in statistical topics. Predictors relating to students' learning behavior in a statistics course such as assignment completion and academic dishonesty are also included as auxiliary variables. Results indicate that the three primary categories of statistical skills—namely, the understanding of statistical concepts, proficiency in selecting appropriate statistical methods, and statistics interpretation skills—can be used to predict students' research competency as demonstrated by their final course scores and letter grades. This study advocates for strategic emphasis on the identified influential topics to enhance efficiency in developing students' research competency. The findings could inform instructors in adopting a strategic approach to teaching the statistical component of research for enhanced efficiency. [ABSTRACT FROM AUTHOR]

Attention is a pivotal component and a central vehicle of mindfulness, a psychological factor improving mental health. Despite architecture's potential to encourage attention and mindfulness, there is still a research gap. This study aimed to investigate architectural design strategies that promotes attention in order to foster mindfulness. The research was carried out in three primary stages. The first step entailed conducting a systematic review by searching publications related to architecture that promotes attention from Scopus in February 2024. After considering the suitability and accessibility, 32 articles were included. No studies were found to have investigated the field of enhancing mindfulness. The second step utilized content analysis to decode the selected articles using a framework developed from literature reviews. All three coders decoded the data independently, allowing the main researcher to compile it into the final dataset. Finally, the data underwent Python meta-analysis for word frequency and association. The result revealed certain qualities that help achieve attention through architecture. The architectural atmosphere is most effective when it features natural forms and spaces that evoke a sense of enclosure. The lighting should emphasize natural light and uniformity, whereas the sound designs primarily concern acoustics, ambient, and noises, with controlled weather emphasizing air aspects. The building should utilize natural materials and incorporate object elements; the facade and entrance are particularly crucial components. Moreover, the colors of brick and green and views encompassing gardens and vegetation are among the qualities mentioned. Based on the analysis, the material, view, and color features were most congruent with the biophilic design concept. All these factors are expected to foster mindfulness, thereby improving mental health. [ABSTRACT FROM AUTHOR]

The methodological purpose of this article is to generate practical guidance for researchers studying complex phenomena through an adaptive case study-mixed methods (CS-MM) design. We describe CS-MM design adjustments made in response to our rapidly changeable research conditions that make complex phenomena challenging to study. We leverage Guetterman & Fetters' (2018) CS-MM design recommendations while discussing specific adaptive design practice areas and advancing new insights gleaned from an expanded 21-month timeline when compared with a previous 4-month CS-MM study. We draw upon Mike Fetters' extensive scholarly contributions while striving to continue his legacy for a better world and embodying Mike's "kind mentoring" approach in our team-based study of the complex phenomena involving the response to a public health emergency. [ABSTRACT FROM AUTHOR]

This study investigates aims to compare communication methods on the perception of sadness. We aim to compare the influence of communication method of Face-to-face (F2F) and Facebook on the intensity of sadness perceived by the receivers. A total number of 112 participants who are students of Thammasat University whom enrolled in the course of general psychology (2nd semester of 2558) was chosen by the method of non-probability sampling; convenience sampling. After that, the subjects were divided into 2 groups and each group received a message in different ways (F2F and Facebook). Then, the participants were required to complete the questionnaire measuring the emotion they perceived. The questionnaire consists of 3 parts: the first part required participants to identify the emotion they perceived from the sender, the second required participants to rate the intensity of the emotion they perceived by using Affective Dimension of Valence from Self-Assessment Manikin (SAM) which has r = .857 when comparing with Affective Slider and r = .97 when comparing SAM with Semantic Differential Scale, and the final part required participants to describe the factor they used to perceive the emotion stated above. Although all of the participants could perceive the emotion of sadness, descriptive statistics suggested that 55.30% of the participants that received the message via F2F perceived the emotion by observing non-verbal cues of the sender, whereas 80.30% of the participants that received the message from Facebook perceived the emotion through viewing the text. An independent sample t-test found that the F2F group perceived sadness at a significantly greater degree than the Facebook group. (t = 2.280, p = 0.0125) [ABSTRACT FROM AUTHOR]