J. Learn. Anal.最新文献

{"title":"Networks and Learning: A View from Physics","authors":"A. Traxler","doi":"10.18608/jla.2022.7669","DOIUrl":"https://doi.org/10.18608/jla.2022.7669","url":null,"abstract":"Like learning analytics, physics education research is a relatively young field that draws on perspectives from multiple disciplines. Network analysis has an even more heterodox perspective, with roots in mathematics, sociology, and, more recently, computer science and physics. This paper reviews how network analysis has been used in physics education research and how it connects to some of the work in this special issue. Insights from physics education research suggest combining social and interaction networks with other data sources and looking for finer-grained details to use in constructing networks, and learning analytics is promising for both avenues. The discussion ends by looking at the complications with incorporating gender into network analysis, and finally the possibilities for the future.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124898821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating Cognitive Network Models of Learners' Knowledge Representations","authors":"Cynthia S. Q. Siew","doi":"10.18608/jla.2022.7671","DOIUrl":"https://doi.org/10.18608/jla.2022.7671","url":null,"abstract":"This commentary discusses how research approaches from Cognitive Network Science can be of relevance to research in the field of Learning Analytics, with a focus on modelling the knowledge representations of learners and students as a network of interrelated concepts. After providing a brief overview of research in Cognitive Network Science, I suggest that a focus on the cognitive processes that occur in the knowledge network, as well as the mechanisms that give rise to changes in the structure of knowledge networks, can lead to potentially informative insights into how learners navigate their knowledge representations to retrieve information and how the knowledge representations of learners develop and grow over the course of their educational careers. Learning Analytics can leverage these insights to design adaptive learning or online learning platforms that optimize learning, and inform pedagogical practice and assessment design that support the development of effective and robust knowledge structures.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130105072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Video Features, Engagement, and Patterns of Collective Attention Allocation: An Open Flow Network Perspective","authors":"Jingjing Zhang, Yicheng Huang, M. Gao","doi":"10.18608/jla.2022.7421","DOIUrl":"https://doi.org/10.18608/jla.2022.7421","url":null,"abstract":"Network analytics has the potential to examine new behaviour patterns that are often hidden by the complexity of online interactions. One of the varied network analytics approaches and methods, the model of collective attention, takes an ecological system perspective to exploring the dynamic process of participation patterns in online and flexible learning environments. This study selected “Fundamentals of C++ programming (Spring 2019)” on XuetangX as an example through which to observe the allocation patterns of attention within MOOC videos, as well as how video features and engagement correlate with the accumulation, circulation, and dissipation pattern of collective attention. The results showed that the types of instructions in videos predicted attention allocation patterns, but they did not predict the engagement of video watching. Instead, the length and whether the full screen was used in the videos had a strong impact on engagement. Learners were more likely to reach a high level of engagement in video watching when their attention had been circulated around the videos. The results imply that understanding the patterns and dynamics of attention flow and how learners engage with videos will allow us to design cost-effective learning resources to prevent learners from becoming overloaded.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126340443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge Transfer in a Two-Mode Network Between Higher Education Teachers and Their Innovative Teaching Projects","authors":"Elena Stasewitsch, L. Barthauer, S. Kauffeld","doi":"10.18608/jla.2022.7427","DOIUrl":"https://doi.org/10.18608/jla.2022.7427","url":null,"abstract":"Knowledge transfer (KT) and innovation diffusion are closely related to each other because it is knowledge regarding an innovation that gets adopted. Little research in learning analytics provides insight into KT processes in two-mode networks, especially in the context of educational innovations. It is unclear how such networks are structured and whether funding can create a network structure efficient for KT. We used a case-study approach to analyze a two-mode network of 208 university members (based on archival data) who worked together on 91 innovative teaching projects. Our results show that the two-mode network displays a decentralized structure and more clustering than can be assumed by chance, promoting KT and learning. To gain a deeper understanding of the kind of knowledge that is transferred in the network, we analyzed the effects of different educational innovation elements (e.g., game-based learning) as attributes of higher education teachers. Overall, our results suggest that funding and the creation of project structures in the context of educational innovation is a sustainable way to create KT, and therefore organizational change. Furthermore, the results imply that university practitioners need to implement networking interventions to create more connections between subgroups in teacher-related networks.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131889945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What Makes Learning Analytics Research Matter","authors":"A. Wise, Simon Knight, X. Ochoa","doi":"10.18608/jla.2021.7647","DOIUrl":"https://doi.org/10.18608/jla.2021.7647","url":null,"abstract":"The ongoing changes and challenges brought on by the COVID-19 pandemic have exacerbated long-standing inequities in education, leading many to question basic assumptions about how learning can best benefit all students. Thirst for data about learning is at an all-time high, sometimes without commensurate attention to ensuring principles this community has long valued: privacy, transparency, openness, accountability, and fairness. How we navigate this dynamic context is critical for the future of learning analytics. Thinking about the issue through the lens of JLA publications over the last eight years, we highlight the important contributions of “problem-centric” rather than “tool-centric” research. We also value attention (proximal or distal) to the eventual goal of closing the loop, connecting the results of our analyses back to improve the learning from which they were drawn. Finally, we recognize the power of cycles of maturation: using information generated about real-world uses and impacts of a learning analytics tool to guide new iterations of data, analysis, and intervention design. A critical element of context for such work is that the learning problems we identify and choose to work on are never blank slates; they embed societal structures, reflect the influence of past technologies; and have previous enablers, barriers and social mediation acting on them. In that context, we must ask the hard questions: What parts of existing systems is our work challenging? What parts is it reinforcing? Do these effects, intentional or not, align with our values and beliefs? In the end what makes learning analytics matter is our ability to contribute to progress on both immediate and long-standing challenges in learning, not only improving current systems, but also considering alternatives for what is and what could be. This requires including stakeholder voices in tackling important problems of learning with rigorous analytic approaches to promote equitable learning across contexts. This journal provides a central space for the discussion of such issues, acting as a venue for the whole community to share research, practice, data and tools across the learning analytics cycle in pursuit of these goals.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131416331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Era in Multimodal Learning Analytics: Twelve Core Commitments to Ground and Grow MMLA","authors":"M. Worsley, Roberto Martínez-Maldonado, Cynthia A. D'Angelo","doi":"10.18608/jla.2021.7361","DOIUrl":"https://doi.org/10.18608/jla.2021.7361","url":null,"abstract":"Multimodal learning analytics (MMLA) has increasingly been a topic of discussion within the learning analytics community. The Society of Learning Analytics Research is home to the CrossMMLA Special Interest Group and regularly hosts workshops on MMLA during the Learning Analytics Summer Institute (LASI). In this paper, we articulate a set of 12 commitments that we believe are critical for creating effective MMLA innovations. Moreover, as MMLA grows in use, it is important to articulate a set of core commitments that can help guide both MMLA researchers and the broader learning analytics community. The commitments that we describe are deeply rooted in the origins of MMLA and also reflect the ways that MMLA has evolved over the past 10 years. We organize the 12 commitments in terms of (i) data collection, (ii) analysis and inference, and (iii) feedback and data dissemination and argue why these commitments are important for conducting ethical, high-quality MMLA research. Furthermore, in using the language of commitments, we emphasize opportunities for MMLA research to align with established qualitative research methodologies and important concerns from critical studies.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122900432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Narrowing the Feedback Gap: Examining Student Engagement with Personalized and Actionable Feedback Messages","authors":"Hamideh Iraj, Anthea Fudge, Huda J. Khan, M. Faulkner, A. Pardo, Vitomir Kovanovíc","doi":"10.18608/jla.2021.7184","DOIUrl":"https://doi.org/10.18608/jla.2021.7184","url":null,"abstract":"One of the major factors affecting student learning is feedback. Although the importance of feedback has been recognized in educational institutions, dramatic changes - such as bigger class sizes and a more diverse student population - challenged the provision of effective feedback. In light of these changes, educators have increasingly been using new digital tools to provide student feedback, given the broader adoption and availability of these new technologies. However, despite these efforts, most educators have limited insight into the recipience of their feedback and wonder which students engage with feedback. This problem is referred to as the \"feedback gap,\" which is the difference between the potential and actual use of feedback, preventing educators and instructional designers from understanding feedback recipience among students. In this study, a set of trackable call-to-action (CTA) links were embedded in feedback messages focused on learning processes and self-regulation of learning in one fully online marketing course and one blended bioscience course. These links helped us examine the association between feedback engagement and course success. We also conducted two focus groups with students from one of the courses to further examine student perceptions of feedback messages. Our results across both courses revealed that early engagement with feedback is positively associated with passing the course and that most students considered feedback messages helpful in their learning. Our study also found some interesting demographic differences between students regarding their engagement with the feedback messages. Such insight enables instructors to ask \"why\" questions, support students' learning, improve feedback processes, and narrow the gap between potential and actual use of feedback. The practical implications of our findings are further discussed.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130930515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microeconometric Approaches in Exploring the Relationships Between Early Alert Systems and Student Retention: A Case Study of a Regionally Based University in Australia","authors":"Scott Harrison, R. Villano, G. Lynch, George Chen","doi":"10.18608/jla.2021.7087","DOIUrl":"https://doi.org/10.18608/jla.2021.7087","url":null,"abstract":"Early alert systems (EAS) are an important technological tool to help manage and improve student retention. Data spanning 16,091 students over 156 weeks was collected from a regionally based university in Australia to explore various microeconometric approaches that establish links between EAS and student retention outcomes. Controlling for numerous confounding variables, significant relationships between the EAS and student retention were identified. Capturing dynamic relationships between the explanatory variables and the hazard of discontinuing provides new insight into understanding student retention factors. We concluded that survival models are the best methods of understanding student retention when temporal data is available.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114305478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging the Gap Between Theory and Empirical Research in Evaluative Judgment","authors":"Hassan Khosravi, George Gyamfi, Barbara E. Hanna, J. Lodge, Solmaz Abdi","doi":"10.18608/jla.2021.7206","DOIUrl":"https://doi.org/10.18608/jla.2021.7206","url":null,"abstract":"The value of students developing the capacity to accurately judge the quality of their work and that of others has been widely studied and recognized in higher education literature. To date, much of the research and commentary on evaluative judgment has been theoretical and speculative in nature, focusing on perceived benefits and proposing strategies seen to hold the potential to foster evaluative judgment. The efficacy of the strategies remains largely untested. The rise of educational tools and technologies that generate data on learning activities at an unprecedented scale, alongside insights from the learning sciences and learning analytics communities, provides new opportunities for fostering and supporting empirical research on evaluative judgment. Accordingly, this paper offers a conceptual framework and an instantiation of that framework in the form of an educational tool called RiPPLE for data-driven approaches to investigating the enhancement of evaluative judgment. Two case studies, demonstrating how RiPPLE can foster and support empirical research on evaluative judgment, are presented.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128685909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Learning Analytics Dashboards: Automated Drill-Down Recommendations to Support Teacher Data Exploration","authors":"Hassan Khosravi, Shiva Shabaninejad, Aneesha Bakharia, S. Sadiq, M. Indulska, D. Gašević","doi":"10.18608/jla.2021.7279","DOIUrl":"https://doi.org/10.18608/jla.2021.7279","url":null,"abstract":"Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on predictive analytics. While predictive models have been successful in many domains, there is an increasing realization of the inadequacies of using predictive models in decision-making tasks that affect individuals without human oversight. In this paper, we employ a suite of state-of-the-art algorithms, from the online analytics processing, data mining, and process mining domains, to present an alternative human-in-the-loop AI method to enable educators to identify, explore, and use appropriate interventions for subpopulations of students with the highest deviation in performance or learning process compared to the rest of the class. We demonstrate an application of our proposed approach in an existing learning analytics dashboard (LAD) and explore the recommended drill-downs in a course with 875 students. The demonstration provides an example of the recommendations from real course data and shows how recommendations can lead the user to interesting insights. Furthermore, we demonstrate how our approach can be employed to develop intelligent LADs.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127567072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}