Suchergebnisse

AbstractCollaborative knowledge construction (CKC) involved students' sharing of information, improvement of ideas, and construction of collective knowledge. In this process, knowledge extraction and analysis can provide valuable insights into students' knowledge capacities, depths, and levels in order to improve the CKC quality. However, existing studies tended to extract and analyze knowledge from a single perspective (e.g., the number of certain knowledge types and knowledge structures), which failed to demonstrate the complexity and dynamics of knowledge construction and advancement. To fill this gap, this research designed a series of computer-supported collaborative concept mapping (CSCCM) activities to facilitate students' CKC process and then used an integrated approach (i.e., semantic knowledge analysis combined with learning analytics) to extract, analyze, and understand students' knowledge characteristics and evolutionary trends. Results demonstrated that compared to the low-performing pairs, the high-performing pairs mainly discussed knowledge related to the course content, and their knowledge evolution trend was relatively stable. Based on the results, this research provided analytical implications to extract, analyze, and understand students' knowledge and pedagogical implications to promote students' knowledge construction and advancement.

AbstractPair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students' discourses, behaviors, and socio-emotions, it is of critical importance to examine their collaborative patterns from a holistic, multimodal, dynamic perspective. But there is a lack of research investigating the collaborative patterns generated by the multimodality. This research applied multimodal learning analytics (MMLA) to collect 19 undergraduate student pairs' multimodal process and products data to examine different collaborative patterns based on the quantitative, structural, and transitional characteristics. The results revealed four collaborative patterns (i.e., a consensus-achieved pattern, an argumentation-driven pattern, an individual-oriented pattern, and a trial-and-error pattern), associated with different levels of process and summative performances. Theoretical, pedagogical, and analytical implications were provided to guide the future research and practice.

AbstractDue to an overwhelming amount of student-generated forum posts in small private online courses (SPOCs), students and instructors find it time-consuming and challenging to effectively navigate and track valuable information, such as the evolution of topics, emotional and behavioral changes in relation to topics. For solving this problem, this study analyzed plenty of discussion posts using an improved dynamic topic model, Time Information-Emotion Behavior Model (TI-EBTM). Time, emotion, and behavior characteristics were incorporated into the topic modeling process, which allowed for an overview of automatic tracking and understanding of temporal topic changes in SPOC discussion forums. The experiment on data from 30 SPOC courses showed that TI-EBTM outperformed other dynamic topic models and was effective in extracting prominent topics over time. Furthermore, we conducted an in-depth temporal topic analysis to investigate the utility of TI-EBTM in a case study. The results of the case study demonstrated that our methodology and analysis shed light on students' temporal focuses (i.e., the changes of topic intensity and topic content) and reflected the evolution of topics' emotional and behavioral tendencies. For example, students tended to express more negative emotions toward the topic about the method of data query by initiating the conversation at the end of the semester. The analytical results can provide instructors with valuable insights into the development of course forums and enable them to fine-tune course forums to suit students' requirements, which will subsequently be helpful in enhancing discussion interaction and students' learning experience.

AbstractAs a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced computing technologies, AI performance prediction model is widely used to identify at-risk students that tend to fail, establish student-centered learning pathways, and optimize instructional design and development. A majority of the existing AI prediction models focus on the development and optimization of the accuracy of AI algorithms rather than applying AI models to provide student with in-time and continuous feedback and improve the students' learning quality. To fill this gap, this research integrated an AI performance prediction model with learning analytics approaches with a goal to improve student learning effects in a collaborative learning context. Quasi-experimental research was conducted in an online engineering course to examine the differences of students' collaborative learning effect with and without the support of the integrated approach. Results showed that the integrated approach increased student engagement, improved collaborative learning performances, and strengthen student satisfactions about learning. This research made contributions to proposing an integrated approach of AI models and learning analytics (LA) feedback and providing paradigmatic implications for future development of AI-driven learning analytics.

AbstractCollaborative problem-solving (CPS) engages students in solving ill-structured problems, creating group knowledge, and developing self-regulation and collaboration skills. Different scaffoldings, such as minimal-guided, task-oriented, and idea-oriented, can be used to facilitate students' CPS activities, but their effects have not been comprehensively explored. In this research, we use minimally-guided, task-oriented, and idea-oriented scaffoldings to promote Chinese university students' online CPS activities and use a multi-method approach to analyze the effects of three scaffolding on collaboration. The results indicate relatively complicated collaborative processes and outcomes supported by three scaffoldings. It is initially shown that the idea-centered scaffolding strengthens students' connections between idea contribution, metacognitive regulation, and knowledge artifact behaviors, which are critical factors for improving the CPS quality. Based on the empirical research results, we conclude that future instructional design should carefully consider the educational culture, time constraint, and student regulation to better facilitate CPS practices.

AbstractGenerative artificial intelligence provides both opportunities and challenges for higher education. Existing literature has not properly investigated how this technology would impact assessment in higher education. This scoping review took a forward-thinking approach to investigate how generative artificial intelligence transforms assessment in higher education. We used the PRISMA extension for scoping reviews to select articles for review and report the results. In the screening, we retrieved 969 articles and selected 32 empirical studies for analysis. Most of the articles were published in 2023. We used three levels—students, teachers, and institutions—to analyses the articles. Our results suggested that assessment should be transformed to cultivate students' self-regulated learning skills, responsible learning, and integrity. To successfully transform assessment in higher education, the review suggested that (i) teacher professional development activities for assessment, AI, and digital literacy should be provided, (ii) teachers' beliefs about human and AI assessment should be strengthened, and (iii) teachers should be innovative and holistic in their teaching to reflect the assessment transformation. Educational institutions are recommended to review and rethink their assessment policies, as well as provide more inter-disciplinary programs and teaching.