J. Learn. Anal.最新文献_第3页

Learning While Working:: Course Enrollment Behaviour as a Macro-Level Indicator of Learning Management Among Adult Learners 边工作边学习:作为成人学习者学习管理宏观层面指标的课程注册行为

J. Learn. Anal. Pub Date : 2022-11-13 DOI: 10.18608/jla.2022.7625

Corey E. Tatel, Sibley F. Lyndgaard, R. Kanfer, J. Melkers

{"title":"Learning While Working:: Course Enrollment Behaviour as a Macro-Level Indicator of Learning Management Among Adult Learners","authors":"Corey E. Tatel, Sibley F. Lyndgaard, R. Kanfer, J. Melkers","doi":"10.18608/jla.2022.7625","DOIUrl":"https://doi.org/10.18608/jla.2022.7625","url":null,"abstract":"As the demand for lifelong learning increases, many working adults have turned to online graduate education in order to update their skillsets and pursue advanced credentials. Simultaneously, the volume of data available to educators and scholars interested in online learning continues to rise. This study seeks to extend learning analytics applications typically oriented toward understanding student interaction with course content, instructors, and peers to the program level in order to gain insight into the ways in which adult learners manage their learning progress over multiple courses and multiple semesters. Using optimal matching analysis, we identify four distinct profiles of course enrollment behaviour among 1,801 successful graduates of an online master’s program that differ with respect to course load, semesters off, and graduation speed. We found that profiles differed significantly as a function of age and knowledge background, but not with respect to gender, ethnicity, or previous academic performance. Findings indicate the utility of expanding learning analytics focused on the micro-level of analysis to the macro-level of analysis and the utility of grounding learning analytics applications geared toward adult learners in a lifespan development perspective. Implications for program design and educational interventions are discussed.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123686666","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

Examining the Interplay between Self-regulated Learning Activities and Types of Knowledge within a Computer-simulated Environment 在计算机模拟的环境中研究自我调节学习活动和知识类型之间的相互作用

J. Learn. Anal. Pub Date : 2022-10-23 DOI: 10.18608/jla.2022.7571

Shan Li, Xiaoshan Huang, Ting-Hui Wang, Zexuan Pan, Susanne P. Lajoie

{"title":"Examining the Interplay between Self-regulated Learning Activities and Types of Knowledge within a Computer-simulated Environment","authors":"Shan Li, Xiaoshan Huang, Ting-Hui Wang, Zexuan Pan, Susanne P. Lajoie","doi":"10.18608/jla.2022.7571","DOIUrl":"https://doi.org/10.18608/jla.2022.7571","url":null,"abstract":"This study examined the temporal co-occurrences of self-regulated learning (SRL) activities and three types of knowledge (i.e., task information, domain knowledge, and metacognitive knowledge) of 34 medical students who solved two tasks of varying complexity in a computer-simulated environment. Specifically, we explored the effects of task complexity on SRL activities, types of knowledge, and their interplay using epistemic network analysis (ENA). We also compared the differences between high and low performers. The results showed that the use of SRL activities, especially the planning and monitoring activities, was more intensive in a difficult task compared to an easy task. Students also used more domain knowledge to solve the difficult task. For both tasks, domain knowledge and metacognitive knowledge co-occurred most frequently, followed by the co-occurrence of domain knowledge and planning. Nevertheless, the interplay of SRL activities and types of knowledge is generally different between the two tasks. Moreover, we found that high performers used significantly more metacognitive knowledge than low performers in the easy task. However, no significant differences were found in the use of SRL activities between high and low performers in both tasks. This study makes theoretical, methodological, and practical contributions to the area of SRL in clinical reasoning.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"263 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116237721","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

Towards data-informed teaching practice:: A model for integrating analytics with teacher inquiry 迈向以数据为基础的教学实践:将分析与教师探究相结合的模型

J. Learn. Anal. Pub Date : 2022-10-23 DOI: 10.18608/jla.2022.7505

Merike Saar, M. Rodríguez-Triana, L. Prieto

引用次数: 0

Developing a Growth Learning Data Mindset: A Secondary School Approach to Creating a Culture of Data Driven Improvement 发展成长学习的数据心态:一种创建数据驱动改进文化的中学方法

J. Learn. Anal. Pub Date : 2022-08-31 DOI: 10.18608/jla.2022.7377

L. Vigentini, Brad Swibel, Garth Hasler

{"title":"Developing a Growth Learning Data Mindset: A Secondary School Approach to Creating a Culture of Data Driven Improvement","authors":"L. Vigentini, Brad Swibel, Garth Hasler","doi":"10.18608/jla.2022.7377","DOIUrl":"https://doi.org/10.18608/jla.2022.7377","url":null,"abstract":"While Learning Analytics (LA) have gained momentum in higher education, there are still few examples of application in the school sector. Even fewer cases are reported of systematic, organizational adoption to drive the support of student learning trajectories that includes teachers, pastoral leaders, and academic managers. This paper presents one such case — at the intersection of praxis, governance, and evaluation — from a practitioner perspective. The paper describes the added value of data-driven approaches to create a culture of improvement in students and teachers in a comprehensive coeducational independent day school in Sydney. Evaluating the work done over the past five years to develop LA dashboards, the authors reflect on the process, the inspirations coming from theory, and the impact of the dashboards in the secondary school context. The data presented is not experimental in nature but supplies tangible evidence for the systematic evaluation scaffolded using the SHEILA policy framework. The main contribution of the paper is a practical demonstration of how managers in a secondary school drew from existing literature and observed data to 1) reflect on the adoption of LA in schools and 2) connect the dots between theory and practice to support teachers grappling with the trajectories of student learning and development, thus encouraging students to self-regulate their learning","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123454946","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

Design Analytics for Mobile Learning: Scaling up the Classification of Learning Designs Based on Cognitive and Contextual Elements 移动学习的设计分析:基于认知和上下文元素的学习设计分类扩展

J. Learn. Anal. Pub Date : 2022-08-31 DOI: 10.18608/jla.2022.7551

Gerti Pishtari, L. Prieto, M. Rodríguez-Triana, Roberto Martínez-Maldonado

{"title":"Design Analytics for Mobile Learning: Scaling up the Classification of Learning Designs Based on Cognitive and Contextual Elements","authors":"Gerti Pishtari, L. Prieto, M. Rodríguez-Triana, Roberto Martínez-Maldonado","doi":"10.18608/jla.2022.7551","DOIUrl":"https://doi.org/10.18608/jla.2022.7551","url":null,"abstract":"\u0000\u0000\u0000This research was triggered by the identified need in literature for large-scale studies about the kinds of designs that teachers create for mobile learning (m-learning). These studies require analyses of large datasets of learning designs. The common approach followed by researchers when analyzing designs has been to manually classify them following high-level pedagogically guided coding strategies, which demands extensive work. Therefore, the first goal of this paper is to explore the use of supervised machine learning (SML) to automatically classify the textual content of m-learning designs using pedagogically relevant classifications, such as the cognitive level demanded by students to carry out specific designed tasks, the phases of inquiry learning represented in the designs, or the role that the situated environment has in the designs. Because not all SML models are transparent, but researchers often need to understand their behaviour, the second goal of this paper is to consider the trade-off between models’ performance and interpretability in the context of design analytics for m-learning. To achieve these goals, we compiled a dataset of designs deployed using two tools, Avastusrada and Smartzoos. With this dataset, we trained and compared different models and feature extraction techniques. We further optimized and compared the best performing and most interpretable algorithms (EstBERT and Logistic Regression) to consider the second goal with an illustrative case. We found that SML can reliably classify designs with accuracy > 0.86 and Cohen’s kappa > 0.69.\u0000\u0000\u0000","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127452214","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: 2

FoLA2 - A Method for Co-creating Learning Analytics-Supported Learning Design FoLA2 -一种共同创造学习分析支持的学习设计方法

J. Learn. Anal. Pub Date : 2022-08-31 DOI: 10.18608/jla.2022.7643

Marcel Schmitz, Maren Scheffel, Roger Bemelmans, H. Drachsler

引用次数: 3

Toward an Institutional Analytics Agenda for Addressing Student Dropout in Higher Education: An Academic Stakeholders' Perspective 迈向解决高等教育学生辍学问题的制度分析议程:学术利益相关者的视角

J. Learn. Anal. Pub Date : 2022-08-31 DOI: 10.18608/jla.2022.7507

L. H. N. De Silva, Irene-Angelica Chounta, M. Rodríguez-Triana, Eric Roldán Roa, Anne-Katrin Gramberg, A. Valk

{"title":"Toward an Institutional Analytics Agenda for Addressing Student Dropout in Higher Education: An Academic Stakeholders' Perspective","authors":"L. H. N. De Silva, Irene-Angelica Chounta, M. Rodríguez-Triana, Eric Roldán Roa, Anne-Katrin Gramberg, A. Valk","doi":"10.18608/jla.2022.7507","DOIUrl":"https://doi.org/10.18608/jla.2022.7507","url":null,"abstract":"\u0000\u0000\u0000Although the number of students in higher education institutions (HEIs) has increased over the past two decades, it is far from assured that all students will gain an academic degree. To that end, institutional analytics (IA) can offer insights to support strategic planning with the aim of reducing dropout and therefore of minimizing its negative impact (e.g., on students, academic stakeholders, and institutions). However, it is not clear how institutional stakeholders can integrate IA in their practice to overcome academic-related issues and to offer support to students who struggle to achieve their academic goals. To address this gap, we conducted focus groups with 13 institutional stakeholders of an Estonian university. By analyzing the focus group data, we identified three main categories of factors influencing dropout from the perspective of institutional stakeholders: (1) institutional experience, (2) educational goals, and (3) personal aspects. We discuss our findings from an institutional perspective with the aim of reflecting on institutional processes, organizational structures, and facilitatory roles in the context of dropout in higher education (HE). We argue that IA can provide insights into students’ institutional experience, educational goals, and personal aspects to further support decision-making on the institutional level. We envision our findings contributing to a participatory agenda for the design, implementation, and integration of IA solutions focusing on addressing dropout in HE.\u0000\u0000\u0000","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122240653","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

Curriculum Modelling and Learner Simulation as a Tool in Curriculum (Re)Design 课程(再)设计中的课程建模与学习者模拟

J. Learn. Anal. Pub Date : 2022-08-31 DOI: 10.18608/jla.2022.7499

John E. McEneaney, Paul Morsink

{"title":"Curriculum Modelling and Learner Simulation as a Tool in Curriculum (Re)Design","authors":"John E. McEneaney, Paul Morsink","doi":"10.18608/jla.2022.7499","DOIUrl":"https://doi.org/10.18608/jla.2022.7499","url":null,"abstract":"Learning analytics (LA) provides tools to analyze historical data with the goal of better understanding how curricular structures and features have impacted student learning. Forward-looking curriculum design, however, frequently involves a degree of uncertainty. Historical data may be unavailable, a contemplated modification to curriculum may be unprecedented, or we may lack data regarding particular learner populations. To address this need, we propose using curriculum modelling and learner simulation (CMLS), which relies on well-established modelling theory and software to represent an existing or contemplated curriculum. The resulting model incorporates relevant research-based principles of learning to individually simulate learners and estimate their learning achievement as they move through the modelled curriculum. Results reflect both features of the curriculum (e.g., time allocated to different learning outcomes), learner profiles, and the natural variability of learners. We describe simulations with two versions of a college-level curriculum, explaining how results from simulations informed curriculum redesign work. We conclude with commentary on generalizing these methods, noting both theoretical and practical benefits of CMLS for curriculum (re)design.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116305970","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

Designing Affirmative Action Policies under Uncertainty 不确定性下平权行动政策的设计

J. Learn. Anal. Pub Date : 2022-08-31 DOI: 10.18608/jla.2022.7463

Corinna Hertweck

{"title":"Designing Affirmative Action Policies under Uncertainty","authors":"Corinna Hertweck","doi":"10.18608/jla.2022.7463","DOIUrl":"https://doi.org/10.18608/jla.2022.7463","url":null,"abstract":"We study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs. In the context of this system, we explore affirmative action policies that seek to narrow the gap between the admission rates of different socio-demographic groups while still accepting students with high scores. Since there is uncertainty about the score distribution of the students who will apply to each program, it is unclear what policy would have the desired effect on the admission rates of different groups. We address this challenge by using a predictive model trained on historical data to help optimize the parameters of such policies. We find that a learned predictive model does significantly better than relying on the ideal parameters for the last year. At the same time, we also find that a large pool of historical data yields similar results as our predictive approach for our data. Due to the more complex nature of the predictive approach, we conclude that a simpler approach should be preferred if enough data is available (e.g., long-standing, traditional university programs), but not for newer programs and other cases in which our predictive strategy can prove helpful.","PeriodicalId":145357,"journal":{"name":"J. Learn. Anal.","volume":"83 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131376881","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

Using HeuristicsMiner to Analyze Problem-Solving Processes: Exemplary Use Case of a Productive-Failure Study 使用启发式miner分析解决问题的过程:生产性失败研究的示例用例

J. Learn. Anal. Pub Date : 2022-08-09 DOI: 10.18608/jla.2022.7363

Christian Hartmann, N. Rummel, M. Bannert

引用次数: 4