J. Learn. Anal.最新文献_第9页

Learning Design and Learning Analytics: Snapshot 2020 学习设计和学习分析:快照2020

J. Learn. Anal. Pub Date : 2020-12-17 DOI: 10.18608/jla.2020.73.2

Leah P. Macfadyen, Lori Lockyer, B. Rienties

{"title":"Learning Design and Learning Analytics: Snapshot 2020","authors":"Leah P. Macfadyen, Lori Lockyer, B. Rienties","doi":"10.18608/jla.2020.73.2","DOIUrl":"https://doi.org/10.18608/jla.2020.73.2","url":null,"abstract":"“Learning design” belongs to that interesting class of concepts that appear on the surface to be simple and self-explanatory, but which are actually definitionally vague and contested in practice. Like “learning analytics,” the field of learning design aspires to improve teaching practice, the learning experience, and learning outcomes. And like learning analytics, this interdisciplinary field also lacks a shared language, common vocabulary, or agreement over its definition and purpose, resulting in uncertainty even about who its practitioners are — Educators? Designers? Researchers? All of these? (Law, Li, Farias Herrera, Chan & Pong, 2017). Almost a decade ago, however, learning analytics researchers pointed to the rich potential for synergies between learning analytics and learning design (Lockyer & Dawson, 2011). These authors (and others since, as cited below) argued that effective alignment of learning analytics and learning design would benefit both fields, and would offer educators and investigators the evidence they need that their efforts and innovations in learning design are “worth it” in terms of improving teaching practice and learning: \"The integration of research related to both learning design and learning analytics provides the necessary contextual overlay to better understand observed student behavior and provide the necessary pedagogical recommendations where learning behavior deviates from pedagogical intention\" (Lockyer & Dawson, 2011, p. 155).","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"1997 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132490826","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}

引用次数: 22

Synergies of Learning Analytics and Learning Design: A Systematic Review of Student Outcomes 学习分析与学习设计的协同作用:学生成果的系统回顾

J. Learn. Anal. Pub Date : 2020-12-17 DOI: 10.18608/jla.2020.73.3

M. Blumenstein

{"title":"Synergies of Learning Analytics and Learning Design: A Systematic Review of Student Outcomes","authors":"M. Blumenstein","doi":"10.18608/jla.2020.73.3","DOIUrl":"https://doi.org/10.18608/jla.2020.73.3","url":null,"abstract":"The field of learning analytics (LA) has seen a gradual shift from purely data-driven approaches to more holistic views of improving student learning outcomes through data-informed learning design (LD). Despite the growing potential of LA in higher education (HE), the benefits are not yet convincing to the practitioner, in particular aspects of aligning LA data with LD toward desired learning outcomes. This review presents a systematic evaluation of effect sizes reported in 38 key studies in pursuit of effective LA approaches to measuring student learning gain for the enhancement of HE pedagogy and delivery. Large positive effects on student outcomes were found in LDs that fostered socio-collaborative and independent learning skills. Recent trends in personalization of learner feedback identified a need for the integration of student-idiosyncratic factors to improve the student experience and academic outcomes. Finally, key findings are developed into a new three-level framework, the LA Learning Gain Design (LALGD) model, to align meaningful data capture with pedagogical intentions and their learning outcomes. Suitable for various settings — face to face, blended, or fully online — the model contributes to data-informed learning and teaching pedagogies in HE.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116054666","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}

引用次数: 19

Learning Analytics Impact: Critical Conversations on Relevance and Social Responsibility 学习分析的影响:关于相关性和社会责任的关键对话

J. Learn. Anal. Pub Date : 2020-12-17 DOI: 10.18608/jla.2020.73.1

X. Ochoa, Simon Knight, A. Wise

{"title":"Learning Analytics Impact: Critical Conversations on Relevance and Social Responsibility","authors":"X. Ochoa, Simon Knight, A. Wise","doi":"10.18608/jla.2020.73.1","DOIUrl":"https://doi.org/10.18608/jla.2020.73.1","url":null,"abstract":"Our 2019 editorial opened a dialogue about what is needed to foster an impactful field of learning analytics (Knight, Wise, & Ochoa, 2019). As we head toward the close of a tumultuous year that has raised profound questions about the structure and processes of formal education and its role in society, this conversation is more relevant than ever. That editorial, and a recent online community event, focused on one component of the impact: standards for scientific rigour and the criteria by which knowledge claims in an interdisciplinary, multi-methodology field should be judged. These initial conversations revealed important commonalities across statistical, computational, and qualitative approaches in terms of a need for greater explanation and justification of choices in using appropriate data, models, or other methodological approaches, as well as the many micro-decisions made in applying specific methodologies to specific studies. The conversations also emphasize the need to perform different checks (for overfitting, for bias, for replicability, for the contextual bounds of applicability, for disconfirming cases) and the importance of learning analytics research being relevant by situating itself within a set of educational values, making tighter connections to theory, and considering its practical mobilization to affect learning. These ideas will serve as the starting point for a series of detailed follow-up conversations across the community, with the goal of generating updated standards and guidance for JLA articles.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116593057","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}

引用次数: 7

How Can Predictive Learning Analytics and Motivational Interventions Increase Student Retention and Enhance Administrative Support in Distance Education? 预测学习分析和动机干预如何在远程教育中提高学生保留率和加强行政支持?

J. Learn. Anal. Pub Date : 2020-09-19 DOI: 10.18608/JLA.2020.72.4

C. Herodotou, G. Naydenova, Avinash Boroowa, Alison Gilmour, B. Rienties

{"title":"How Can Predictive Learning Analytics and Motivational Interventions Increase Student Retention and Enhance Administrative Support in Distance Education?","authors":"C. Herodotou, G. Naydenova, Avinash Boroowa, Alison Gilmour, B. Rienties","doi":"10.18608/JLA.2020.72.4","DOIUrl":"https://doi.org/10.18608/JLA.2020.72.4","url":null,"abstract":"Despite the potential of Predictive Learning Analytics (PLAs) to identify students at risk of failing their studies, research demonstrating effective application of PLAs to higher education is relatively limited. The aims of this study are 1) to identify whether and how PLAs can inform the design of motivational interventions and 2) to capture the impact of those interventions on student retention at the Open University UK. A predictive model — the Student Probabilities Model (SPM) — was used to predict the likelihood of a student remaining in a course at the next milestone and eventually completing it. Undergraduate students (N=630) with a low probability of completing their studies were randomly allocated into the control (n=312) and intervention groups (n=318), and contacted by the university Student Support Teams (SSTs) using a set of motivational interventions such as text, phone, and email. The results of the randomized control trial showed statistically significant better student retention outcomes for the intervention group, with the proposed intervention deemed effective in facilitating course completion. The intervention also improved the administration of student support at scale and low cost.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130292319","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}

引用次数: 19

Analytics of Learning Strategies: Role of Course Design and Delivery Modality 学习策略分析:课程设计与交付方式的作用

J. Learn. Anal. Pub Date : 2020-09-19 DOI: 10.18608/JLA.2020.72.3

W. Matcha, D. Gašević, Nora'ayu Ahmad Uzir, J. Jovanović, A. Pardo, Lisa-Angelique Lim, Jorge Maldonado-Mahauad, Sheridan Gentili, M. Pérez-Sanagustín, Yi-Shan Tsai

{"title":"Analytics of Learning Strategies: Role of Course Design and Delivery Modality","authors":"W. Matcha, D. Gašević, Nora'ayu Ahmad Uzir, J. Jovanović, A. Pardo, Lisa-Angelique Lim, Jorge Maldonado-Mahauad, Sheridan Gentili, M. Pérez-Sanagustín, Yi-Shan Tsai","doi":"10.18608/JLA.2020.72.3","DOIUrl":"https://doi.org/10.18608/JLA.2020.72.3","url":null,"abstract":"Generalizability of the value of methods based on learning analytics remains one of the big challenges in the field of learning analytics. One approach to testing generalizability of a method is to apply it consistently in different learning contexts. This study extends a previously published work by examining the generalizability of a learning analytics method proposed for detecting learning tactics and strategies from trace data. The method was applied to the datasets collected in three different course designs and delivery modalities, including flipped classroom, blended learning, and massive open online course. The proposed method combines process mining and sequence analysis. The detected learning strategies are explored in terms of their association with academic performance. The results indicate the applicability of the proposed method across different learning contexts. Moreover, the findings contribute to the understanding of the learning tactics and strategies identified in the trace data: learning tactics proved to be responsive to the course design, whereas learning strategies were found to be more sensitive to the delivery modalities than to the course design. These findings, well aligned with self-regulated learning theory, highlight the association of learning contexts with the choice of learning tactics and strategies.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124663990","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}

引用次数: 34

Real-Time Prediction of Students' Activity Progress and Completion Rates 实时预测学生的活动进度和完成率

J. Learn. Anal. Pub Date : 2020-09-19 DOI: 10.18608/JLA.2020.72.2

Louis Faucon, Jennifer K. Olsen, Stian Håklev, P. Dillenbourg

{"title":"Real-Time Prediction of Students' Activity Progress and Completion Rates","authors":"Louis Faucon, Jennifer K. Olsen, Stian Håklev, P. Dillenbourg","doi":"10.18608/JLA.2020.72.2","DOIUrl":"https://doi.org/10.18608/JLA.2020.72.2","url":null,"abstract":"In classrooms, some transitions between activities impose (quasi-)synchronicity, meaning there is a need for learners to move between activities at the same time. To make real-time decisions about when to move to the next activity, teachers need to be able to balance the progress of their students as they work at different paces. In this paper, we present a set of estimators that can be used in real time to predict the progress and completion rates of students working on computer-supported activities that can be divided into sequential subtasks. With our estimators, we investigate what effect the average progress rate of the class, a given number of previous steps, or weighting the proportion of progress assigned to each subtask has on predictions of students’ progress. We find that accounting for the average class progress rate near the beginning of the activity can improve predictions over baseline. Additionally, weighted subtasks decrease prediction accuracy for activities where the behaviour of faster students diverges from the average behaviour of the class. This paper contributes to our ability to provide accurate student progress predictions and to understand the behaviour of students as they progress through the activity. These real-time predictions can enable teachers to optimize learning time in their classrooms.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126784343","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}

引用次数: 9

Scaling the Student Journey from Course-Level Information to Program Level Progression and Graduation: A Model 从课程级信息到项目级进展和毕业的学生旅程:一个模型

J. Learn. Anal. Pub Date : 2020-09-19 DOI: 10.18608/JLA.2020.72.5

Pablo Munguia, Amelia Brennan

引用次数: 3

Utilizing Student Time Series Behaviour in Learning Management Systems for Early Prediction of Course Performance 在学习管理系统中利用学生时间序列行为进行课程表现的早期预测

J. Learn. Anal. Pub Date : 2020-09-19 DOI: 10.18608/JLA.2020.72.1

Fu Chen, Ying Cui

{"title":"Utilizing Student Time Series Behaviour in Learning Management Systems for Early Prediction of Course Performance","authors":"Fu Chen, Ying Cui","doi":"10.18608/JLA.2020.72.1","DOIUrl":"https://doi.org/10.18608/JLA.2020.72.1","url":null,"abstract":"Predictive analytics in higher education has become increasingly popular in recent years with the growing availability of educational big data. Particularly, a wealth of student activity data is available from learning management systems (LMSs) in most academic institutions. However, previous investigations into predictive analytics in higher education using LMS activity data did not adequately accommodate student behaviours in the form of time series. In this study, we have applied a deep learning approach — long short-term memory (LSTM) networks — to analyze student online temporal behaviours using their LMS data for the early prediction of course performance. To reveal the potential of the deep learning approach in predictive analytics, we compared LSTM networks with eight conventional machine learning classifiers in terms of the prediction performance as measured by the area under the ROC (receiver operating characteristic) curve (AUC) scores. Results indicate that using the deep learning approach, time series information about click frequencies successfully provided early detection of at-risk students with moderate prediction accuracy. In addition, the deep learning approach showed higher prediction performance and stronger generalizability than the machine learning classifiers.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116683784","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}

引用次数: 41

Editorial: Beyond Cognitive Ability 社论:超越认知能力

J. Learn. Anal. Pub Date : 2020-04-03 DOI: 10.18608/jla.2020.71.1

Srécko Joksimovíc, George Siemens, Yuan Wang, M. O. S. Pedro, Jason D. Way

引用次数: 11

Can School Enrolment and Performance be Improved by Maximizing Students' Sense of Choice in Elective Subjects? 最大限度地提高学生对选修科目的选择意识，能否提高学校的入学率和绩效?

J. Learn. Anal. Pub Date : 2020-04-01 DOI: 10.18608/jla.2020.71.6

Rhyd Lewis, Tom Anderson, Fiona Carroll

引用次数: 1