Shuang Li

Adolescent mental health is foundational to personal development, yet it faces escalating challenges globally. While traditional assessment methods lack objectivity and ecological validity, integrating computer-assisted technology (CAT) into performance-based assessments (PBAs) offers a promising pathway. This review, following the PRISMA-ScR reporting standard, analyzed 89 articles (2015-2025) to map the assessed components, CAT applications, and scenario diversity in mental health PBAs. Analysis revealed a research emphasis on mental disorders, with critical domains for adolescent development remaining significantly understudied. CATs significantly enhanced PBAs through data analysis, data acquisition, scenario creation, and tool digitization. PBA scenarios are diverse, demonstrating the adaptability of PBAs for multidimensional mental health assessment. Prioritizing the design of PBAs for social-emotional and adaptive assessment is critical for the early identification of adolescent mental health issues. Furthermore, advancing predictive analytics and leveraging large language models for feedback generation are promising ways to unlock CAT's potential in enhancing PBAs. Importantly, integrating and adapting scenarios from validated scales by CATs into PBAs could further enhance assessment typicality and reliability.

Background: Enhancing the effectiveness of online learning has become a key challenge with regard to the ability of blended learning to reach its full potential. However, mechanisms by which students' self‐regulated learning (SRL) skills influence their online learning engagement in blended learning and subsequent learning achievement have yet to be explored fully. Objectives: This study aims to explore the mechanisms underlying the effects of students' SRL skills on online learning behaviour and learning achievement in blended learning. Methods: Based on students' self‐reported data and learning behavioural data, this study employed a combination of structural equation modelling and learning analytics to conduct an in‐depth investigation into the influence of SRL skills on online learning behaviour and blended learning achievement. Results: The results indicated that the duration spent on challenging mandatory tasks mediates the relationship between psychological engagement and learning achievement; Two SRL skills, task strategies and self‐evaluation, played positive moderating roles in the relationship between psychological engagement and online behavioural engagement. Further analysis of behavioural sequences revealed that these two skills influenced students' behavioural patterns during video learning and their effectiveness in error attribution during exercises. Additionally, self‐evaluation impacted the approach to quizzes and error correction during exercises. Takeaway: The findings of this research enrich and advance the understanding of characteristics and processes of SRL in blended learning contexts and have important implications for both instructional design and learning support in the context of blended K‐12 learning.

To inform the relationships among interaction patterns, social capital accumulation, and learning benefits, based on 29,056 log data from a cMOOC (Connectivist Massive Open Online Course) in China, this study examined the difference in social capital accumulation and content production among different interaction patterns using cluster analysis, lag sequence analysis, social network analysis, and the Kruskal–Wallis test. Five types of interaction patterns in connectivist learning were identified: “creative connected participants”, “active connected participants”, “poorly engaged social participants”, “poorly engaged moderate participants”, and “resource investigating participants”. The results demonstrated that participants’ interaction patterns influence their social capital accumulation and content production and that positive interaction engagement would compensate for the disadvantage of participants’ initial position in social capital accumulation. Furthermore, resource access, social interaction, and content release had different roles in the accumulation of social capital. The pattern with higher engagement in all three kinds of interaction activities was associated with better content production because learners accumulated more bonding capital and bridging capital, which would bring them more returns in content creation. This study highlights the connectivist learning mechanism and effectiveness from the social capital perspective and provides valuable insights into the design and support of connectivist learning.

Aim: Student engagement is an important factor to the success of higher education. This study aimed to develop a Generic Student Engagement Scale (GSES) for face‐to‐face and online learning.Design: This was a cross‐sectional psychometric study.Methods: We tested the psychometric properties of GSES in 451 students at the school of nursing and health studies undertaking online and face‐to‐face learning at a local university in Hong Kong between 2016 and 2018.Results: Content validity, face validity and test–retest reliability of GSES were satisfactory. The 29‐item GSES contains five factors “self‐regulated learning,” “cognitive strategy use,” “experienced emotion,” “teacher–student interaction,” and “enjoyment of school life” with the good model fit. The GSES is a reliable and valid psychometric instrument to measure student engagement in face‐to‐face and online learning among undergraduates and higher diploma students. Our results implied that student engagement can be assessed in routine or research by using our instrument.