Int. J. Learn. Anal. Artif. Intell. Educ.最新文献

Information Extraction from Binary Skill Assessment Data with Machine Learning 基于机器学习的二元技能评估数据信息提取

Int. J. Learn. Anal. Artif. Intell. Educ. Pub Date : 2021-08-12 DOI: 10.3991/ijai.v3i1.24295

S. Jauhiainen, T. Krosshaug, E. Petushek, Jukka-Pekka Kauppi, S. Äyrämö

{"title":"Information Extraction from Binary Skill Assessment Data with Machine Learning","authors":"S. Jauhiainen, T. Krosshaug, E. Petushek, Jukka-Pekka Kauppi, S. Äyrämö","doi":"10.3991/ijai.v3i1.24295","DOIUrl":"https://doi.org/10.3991/ijai.v3i1.24295","url":null,"abstract":"Strength training exercises are essential for rehabilitation, improving our health as well as in sports. For optimal and safe training, educators and trainers in the industry should comprehend exercise form or technique. Currently, there is a lack of tools measuring in-depth skills of strength training experts. In this study, we investigate how data mining methods can be used to identify novel and useful skill patterns from a binary multiple choice questionnaire test designed to measure the knowledge level of strength training experts. A skill test assessing exercise technique expertise and comprehension was answered by 507 fitness professionals with varying backgrounds. A triangulated approach of clustering and non-negative matrix factorization (NMF) was used to discover skill patterns among participants and patterns in test questions. Four distinct participant subgroups were identified in data with clustering and further question patterns with NMF. The results can be used to, for example, identify missing skills and knowledge in participants and subgroups of participants and form general and personalized or background specific guidelines for future education. In addition, the test can be optimized based on, for example, if some questions can be answered correct even without the required skill or if they seem to be measuring overlapping skills. Finally, this approach can be utilized with other multiple choice test data in future educational research.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121217068","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: 0

Redesigning a First Year Physiology Course using Learning Analytics to Improve Student Performance 利用学习分析重新设计一年级生理学课程以提高学生成绩

Int. J. Learn. Anal. Artif. Intell. Educ. Pub Date : 2021-03-10 DOI: 10.3991/IJAI.V3I1.21799

Mark T. Williams, L. Lluka, Prasad Chunduri

{"title":"Redesigning a First Year Physiology Course using Learning Analytics to Improve Student Performance","authors":"Mark T. Williams, L. Lluka, Prasad Chunduri","doi":"10.3991/IJAI.V3I1.21799","DOIUrl":"https://doi.org/10.3991/IJAI.V3I1.21799","url":null,"abstract":"Learning analytics (LA), a fast emerging concept in higher education, is used to understand and optimize the student learning process and the envi-ronment in which it occurs. Knowledge obtained from the LA paradigm is often utilized to construct statistical models aimed at identifying students who are at risk of failing the unit/course, and to subsequently design inter-ventions that are targeted towards improving the course outcomes for these students. In previous studies, models were constructed using a wide variety of variables, but emerging evidence suggests that the models constructed us-ing course-specific variables are more accurate, and provide a better under-standing of the learning context. For our current study, student performance in the various course assessment tasks was used as a basis for the predictive models and future intervention design, as they are conventionally used to evaluate student learning outcomes and the degree to which the various course learning objectives are met. Further, students in our course are pri-marily first-year university students, who are still unfamiliar with the learning and assessment context of higher education, and this prevents them from adequately preparing for the tasks, and consequently reduces their course performance and outcome. We first constructed statistical models that would be used to identify students who are at risk of failing the course and to identify assessment tasks that students in our course find challeng-ing, as a guide for the design of future interventional activities. Every con-structed predictive model had an excellent capacity to discriminate between students who passed the course and those who failed. Analysis revealed that not only at-risk students, but the whole cohort, would benefit from in-terventions improving their conceptual understanding and ability to con-struct high-scoring answers to Short Answer Questions.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123663028","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: 1

Improving Students Performance in Small-Scale Online Courses - A Machine Learning-Based Intervention 提高学生在小规模在线课程中的表现——一种基于机器学习的干预

Int. J. Learn. Anal. Artif. Intell. Educ. Pub Date : 2020-11-23 DOI: 10.3991/ijai.v2i2.19371

Sepinoud Azimi, Carmen-Gabriela Popa, Tatjana Cuci'c

{"title":"Improving Students Performance in Small-Scale Online Courses - A Machine Learning-Based Intervention","authors":"Sepinoud Azimi, Carmen-Gabriela Popa, Tatjana Cuci'c","doi":"10.3991/ijai.v2i2.19371","DOIUrl":"https://doi.org/10.3991/ijai.v2i2.19371","url":null,"abstract":"The birth of massive open online courses (MOOCs) has had an undeniable effect on how teaching is being delivered. It seems that traditional in-class teaching is becoming less popular with the young generation – the generation that wants to choose when, where and at what pace they are learning. As such, many universities are moving towards taking their courses, at least partially, online. However, online courses, although very appealing to the younger generation of learners, come at a cost. For example, the dropout rate of such courses are higher than that of more traditional ones, and the reduced in-person interaction with the teachers results in less timely guidance and intervention from the educators. Machine learning (ML)-based approaches have shown phenomenal successes in other domains. The existing stigma that applying ML-based techniques requires a large amount of data seems to be a bottleneck when dealing with small-scale courses with limited amounts of produced data. In this study, we show not only that the data collected from an online learning management system could be well utilized in order to predict students’ overall performance but also that it could be used to propose timely intervention strategies to boost the students’ performance level. The results of this study indicate that effective intervention strategies could be suggested as early as the middle of the course to change the course of students’ progress for the better. We also present an assistive pedagogical tool based on the outcome of this study, to assist in identifying challenging students and in suggesting early intervention strategies.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"751 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116108198","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: 4

Learning Analytics for Blended Learning: A Systematic Review of Theory, Methodology, and Ethical Considerations 混合式学习的学习分析:理论、方法和伦理考虑的系统回顾

Int. J. Learn. Anal. Artif. Intell. Educ. Pub Date : 2020-10-29 DOI: 10.3991/ijai.v2i2.17887

Nina Bergdahl, Jalal Nouri, Thashmee Karunaratne, M. Afzaal, Mohammed Saqr

{"title":"Learning Analytics for Blended Learning: A Systematic Review of Theory, Methodology, and Ethical Considerations","authors":"Nina Bergdahl, Jalal Nouri, Thashmee Karunaratne, M. Afzaal, Mohammed Saqr","doi":"10.3991/ijai.v2i2.17887","DOIUrl":"https://doi.org/10.3991/ijai.v2i2.17887","url":null,"abstract":"Learning Analytics (LA) approaches in Blended Learning (BL) research is becoming an established field. In the light of previous critiqued toward LA for not being grounded in theory, the General Data Protection and a renewed focus on individuals’ integrity, this review aims to explore the use of theories, the methodological and analytic approaches in educational settings, along with surveying ethical and legal considerations. The review also maps and explores the outcomes and discusses the pitfalls and potentials currently seen in the field. Journal articles and conference papers were identified through systematic search across relevant databases. 70 papers met the inclusion criteria: they applied LA within a BL setting, were peer-reviewed, full-papers, and if they were in English. The results reveal that the use of theoretical and methodological approaches was disperse, we identified approaches of BL not included in categories of BL in existing BL literature and suggest these may be referred to as hybrid blended learning, that ethical considerations and legal requirements have often been overlooked. We highlight critical issues that contribute to raise awareness and inform alignment for future research to ameliorate diffuse applications within the field of LA.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117038427","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

Recommender - Potentials and Limitations for Self-Study in Higher Education from an Educational Science Perspective 推荐人——教育科学视角下高等教育自学的潜力与局限

Int. J. Learn. Anal. Artif. Intell. Educ. Pub Date : 2020-09-25 DOI: 10.3991/IJAI.V2I2.14763

Christina Gloerfeld, Silke Wrede, C. D. Witt, Xia Wang

引用次数: 3

Fearing the Robot Apocalypse: Correlates of AI Anxiety 担心机器人末日:人工智能焦虑的相关因素

Int. J. Learn. Anal. Artif. Intell. Educ. Pub Date : 2020-08-27 DOI: 10.3991/ijai.v2i2.16759

D. Lemay, Ram B. Basnet, Tenzin Doleck

引用次数: 5

Reflections on Different Learning Analytics Indicators for Supporting Study Success 对支持学习成功的不同学习分析指标的思考

Int. J. Learn. Anal. Artif. Intell. Educ. Pub Date : 2020-07-06 DOI: 10.3991/ijai.v2i2.15639

Dirk Ifenthaler, J. Yau

{"title":"Reflections on Different Learning Analytics Indicators for Supporting Study Success","authors":"Dirk Ifenthaler, J. Yau","doi":"10.3991/ijai.v2i2.15639","DOIUrl":"https://doi.org/10.3991/ijai.v2i2.15639","url":null,"abstract":"Common factors, which are related to study success include students’ sociodemographic factors, cognitive capacity, or prior academic performance, and individual attributes as well as course related factors such as active learning and attention or environmental factors related to supportive academic and social embeddedness. In addition, there are various stages of a learner’s learning journey from the beginning when commencing learning until its completion, as well as different indicators or variables that can be examined to gauge or predict how successfully that journey can or will be at different points during that journey, or how successful learners may complete the study and thereby acquiring the intended learning outcomes. The aim of this research is to gain a deeper understanding of not only if learning analytics can support study success, but which aspects of a learner’s learning journey can benefit from the utilisation of learning analytics. We, therefore, examined different learning analytics indicators to show which aspect of the learning journey they were successfully supporting. Key indicators may include GPA, learning history, and clickstream data. Depending on the type of higher education institution, and the mode of education (face-to-face and/or distance), the chosen indicators may be different due to them having different importance in predicting the learning outcomes and study success.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124462712","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: 15

Online Learning Communities in the COVID-19 Pandemic: Social Learning Network Analysis of Twitter During the Shutdown COVID-19大流行中的在线学习社区:关闭期间Twitter的社会学习网络分析

Int. J. Learn. Anal. Artif. Intell. Educ. Pub Date : 2020-06-16 DOI: 10.3991/IJAI.V2I1.15427

D. Lemay, Tenzin Doleck

引用次数: 20

A Bibliometric Analysis of the Papers Published in the Journal of Artificial Intelligence in Education from 2015-2019 2015-2019年《教育中的人工智能》期刊论文计量分析

Int. J. Learn. Anal. Artif. Intell. Educ. Pub Date : 2020-05-11 DOI: 10.3991/ijai.v2i1.14481

Clare Baek, Tenzin Doleck

{"title":"A Bibliometric Analysis of the Papers Published in the Journal of Artificial Intelligence in Education from 2015-2019","authors":"Clare Baek, Tenzin Doleck","doi":"10.3991/ijai.v2i1.14481","DOIUrl":"https://doi.org/10.3991/ijai.v2i1.14481","url":null,"abstract":"To analyze the current research status and trends of the artificial intelligence in education field, we applied bibliometric methods to examine the articles published in one of the representative journals of the field, International Journal of Artificial Intelligence in Education, from 2015 to 2019. We analyzed 135 articles retrieved from the Web of Science database and examined prolific countries, collaboration networks, prolific authors, keywords, and the citations the articles received. Through examining keywords, we found that the authors largely focused on students and learning. Through examining prolific authors and countries, we found active publication of corresponding authors from United States, United Kingdom, Canada, and Germany. We found international collaboration among some researchers and institutions, such as strong collaboration network between United States and Canada. We suggest reinforcement in building more widespread international partnership and expanding collaboration network by including diverse institutions. International collaboration and expanded institutional network can improve research by incorporating various perspectives and expertise.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129475873","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: 12

A Capability Model for Learning Analytics Adoption: Identifying Organizational Capabilities from Literature on Learning Analytics, Big Data Analytics, and Business Analytics 采用学习分析的能力模型:从学习分析、大数据分析和商业分析的文献中识别组织能力

Int. J. Learn. Anal. Artif. Intell. Educ. Pub Date : 2020-03-20 DOI: 10.3991/ijai.v2i1.12793

J. Knobbout, Esther van der Stappen

{"title":"A Capability Model for Learning Analytics Adoption: Identifying Organizational Capabilities from Literature on Learning Analytics, Big Data Analytics, and Business Analytics","authors":"J. Knobbout, Esther van der Stappen","doi":"10.3991/ijai.v2i1.12793","DOIUrl":"https://doi.org/10.3991/ijai.v2i1.12793","url":null,"abstract":"Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?” Our research is grounded in resource-based view theory and we extend the scope beyond the field of learning analytics and include capability frameworks for the more mature research fields of big data analytics and business analytics. This paper’s contribution is twofold: 1) it provides a literature review on known capabilities for big data analytics, business analytics, and learning analytics and 2) it introduces a capability model to support the implementation and uptake of learning analytics. During our study, we identified and analyzed 15 key studies. By synthesizing the results, we found 34 organizational capabilities important to the adoption of analytical activities within an institution and provide 461 ways to operationalize these capabilities. Five categories of capabilities can be distinguished – Data, Management, People, Technology, and Privacy & Ethics. Capabilities presently absent from existing learning analytics frameworks concern sourcing and integration, market, knowledge, training, automation, and connectivity. Based on the results of the review, we present the Learning Analytics Capability Model: a model that provides senior management and policymakers with concrete operationalizations to build the necessary capabilities for successful learning analytics adoption.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130113518","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: 6