Taciana Pontual Falcão, R. F. Mello, R. Rodrigues, J. Diniz, Yi-Shan Tsai, D. Gašević
Several tools to support learning processes based on educational data have emerged from research on Learning Analytics (LA) in the last few years. These tools aim to support students and instructors in daily activities, and academic managers in making institutional decisions. Although the adoption of LA tools is spreading, the field still needs to deepen the understanding of the contexts where learning takes place, and of the views of the stakeholders involved in implementing and using these tools. In this sense, the SHEILA framework proposes a set of instruments to perform a detailed analysis of the expectations and needs of different stakeholders in higher education institutions, regarding the adoption of LA. Moreover, there is a lacuna in research on stakeholders' expectations from LA outside the Global North. Therefore, this paper reports on the findings of the application of interviews and focus groups, based on the SHEILA framework, with students and teaching staff from a Brazilian public university, to investigate their perceptions of the potential benefits and risks of using LA in higher education in the country. Findings indicate that there is a high interest in using LA for improving the learning experience, in particular, being able to provide personalized feedback, to adapt teaching practices to students' needs, and to make evidence-based pedagogical decisions. From the analysis of these perspectives, we point to opportunities for using LA in Brazilian higher education.
{"title":"Perceptions and expectations about learning analytics from a brazilian higher education institution","authors":"Taciana Pontual Falcão, R. F. Mello, R. Rodrigues, J. Diniz, Yi-Shan Tsai, D. Gašević","doi":"10.1145/3375462.3375478","DOIUrl":"https://doi.org/10.1145/3375462.3375478","url":null,"abstract":"Several tools to support learning processes based on educational data have emerged from research on Learning Analytics (LA) in the last few years. These tools aim to support students and instructors in daily activities, and academic managers in making institutional decisions. Although the adoption of LA tools is spreading, the field still needs to deepen the understanding of the contexts where learning takes place, and of the views of the stakeholders involved in implementing and using these tools. In this sense, the SHEILA framework proposes a set of instruments to perform a detailed analysis of the expectations and needs of different stakeholders in higher education institutions, regarding the adoption of LA. Moreover, there is a lacuna in research on stakeholders' expectations from LA outside the Global North. Therefore, this paper reports on the findings of the application of interviews and focus groups, based on the SHEILA framework, with students and teaching staff from a Brazilian public university, to investigate their perceptions of the potential benefits and risks of using LA in higher education in the country. Findings indicate that there is a high interest in using LA for improving the learning experience, in particular, being able to provide personalized feedback, to adapt teaching practices to students' needs, and to make evidence-based pedagogical decisions. From the analysis of these perspectives, we point to opportunities for using LA in Brazilian higher education.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123756685","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}
Modeling student knowledge is important for assessment design, adaptive testing, curriculum design, and pedagogical intervention. The assessment design community has primarily focused on continuous latent-skill models with strong conditional independence assumptions among knowledge items, while the prerequisite discovery community has developed many models that aim to exploit the interdependence of discrete knowledge items. This paper attempts to bridge the gap by asking, "When does modeling assessment item interdependence improve predictive accuracy?" A novel adaptive testing evaluation framework is introduced that is amenable to techniques from both communities, and an efficient algorithm, Directed Item-Dependence And Confidence Thresholds (DIDACT), is introduced and compared with an Item-Response-Theory based model on several real and synthetic datasets. Experiments suggest that assessments with closely related questions benefit significantly from modeling item interdependence.
{"title":"Applying prerequisite structure inference to adaptive testing","authors":"S. Saarinen, Evan Cater, M. Littman","doi":"10.1145/3375462.3375541","DOIUrl":"https://doi.org/10.1145/3375462.3375541","url":null,"abstract":"Modeling student knowledge is important for assessment design, adaptive testing, curriculum design, and pedagogical intervention. The assessment design community has primarily focused on continuous latent-skill models with strong conditional independence assumptions among knowledge items, while the prerequisite discovery community has developed many models that aim to exploit the interdependence of discrete knowledge items. This paper attempts to bridge the gap by asking, \"When does modeling assessment item interdependence improve predictive accuracy?\" A novel adaptive testing evaluation framework is introduced that is amenable to techniques from both communities, and an efficient algorithm, Directed Item-Dependence And Confidence Thresholds (DIDACT), is introduced and compared with an Item-Response-Theory based model on several real and synthetic datasets. Experiments suggest that assessments with closely related questions benefit significantly from modeling item interdependence.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117023654","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}
Fatemeh Salehian Kia, Stephanie D. Teasley, M. Hatala, S. Karabenick, Matthew Kay
The aim of student-facing dashboards is to support learning by providing students with actionable information and promoting self-regulated learning. We created a new dashboard design aligned with SRL theory, called MyLA, to better understand how students use a learning analytics tool. We conducted sequence analysis on students' interactions with three different visualizations in the dashboard, implemented in a LMS, for a large number of students (860) in ten courses representing different disciplines. To evaluate different students' experiences with the dashboard, we computed chi-squared tests of independence on dashboard users (52%) to find frequent patterns that discriminate students by their differences in academic achievement and self-regulated learning behaviors. The results revealed discriminating patterns in dashboard use among different levels of academic achievement and self-regulated learning, particularly for low achieving students and high self-regulated learners. Our findings highlight the importance of differences in students' experience with a student-facing dashboard, and emphasize that one size does not fit all in the design of learning analytics tools.
{"title":"How patterns of students dashboard use are related to their achievement and self-regulatory engagement","authors":"Fatemeh Salehian Kia, Stephanie D. Teasley, M. Hatala, S. Karabenick, Matthew Kay","doi":"10.1145/3375462.3375472","DOIUrl":"https://doi.org/10.1145/3375462.3375472","url":null,"abstract":"The aim of student-facing dashboards is to support learning by providing students with actionable information and promoting self-regulated learning. We created a new dashboard design aligned with SRL theory, called MyLA, to better understand how students use a learning analytics tool. We conducted sequence analysis on students' interactions with three different visualizations in the dashboard, implemented in a LMS, for a large number of students (860) in ten courses representing different disciplines. To evaluate different students' experiences with the dashboard, we computed chi-squared tests of independence on dashboard users (52%) to find frequent patterns that discriminate students by their differences in academic achievement and self-regulated learning behaviors. The results revealed discriminating patterns in dashboard use among different levels of academic achievement and self-regulated learning, particularly for low achieving students and high self-regulated learners. Our findings highlight the importance of differences in students' experience with a student-facing dashboard, and emphasize that one size does not fit all in the design of learning analytics tools.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130673404","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}
J. Jovanović, S. Dawson, Srécko Joksimovíc, George Siemens
Models and processes developed in learning analytics research are increasing in sophistication and predictive power. However, the ability to translate analytic findings to practice remains problematic. This study aims to address this issue by establishing a model of learner behaviour that is both predictive of student course performance, and easily interpreted by instructors. To achieve this aim, we analysed fine grained trace data (from 3 offerings of an undergraduate online course, N=1068) to establish a comprehensive set of behaviour indicators aligned with the course design. The identified behaviour patterns, which we refer to as observed study strategies, proved to be associated with the student course performance. By examining the observed strategies of high and low performers throughout the course, we identified prototypical pathways associated with course success and failure. The proposed model and approach offers valuable insights for the provision of process-oriented feedback early in the course, and thus can aid learners in developing their capacity to succeed online.
{"title":"Supporting actionable intelligence: reframing the analysis of observed study strategies","authors":"J. Jovanović, S. Dawson, Srécko Joksimovíc, George Siemens","doi":"10.1145/3375462.3375474","DOIUrl":"https://doi.org/10.1145/3375462.3375474","url":null,"abstract":"Models and processes developed in learning analytics research are increasing in sophistication and predictive power. However, the ability to translate analytic findings to practice remains problematic. This study aims to address this issue by establishing a model of learner behaviour that is both predictive of student course performance, and easily interpreted by instructors. To achieve this aim, we analysed fine grained trace data (from 3 offerings of an undergraduate online course, N=1068) to establish a comprehensive set of behaviour indicators aligned with the course design. The identified behaviour patterns, which we refer to as observed study strategies, proved to be associated with the student course performance. By examining the observed strategies of high and low performers throughout the course, we identified prototypical pathways associated with course success and failure. The proposed model and approach offers valuable insights for the provision of process-oriented feedback early in the course, and thus can aid learners in developing their capacity to succeed online.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123592404","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}
David Lang, Guanling Chen, Kathy Mirzaei, A. Paepcke
We explore the relationship between video playback speed and student learning outcomes. Using an experimental design, we present the results of a pre-registered study that assigns users to watch videos at either 1.0x or 1.25x speed. We find that students who consume sped content are more likely to get better grades in a course, attempt more content, and obtain more certificates. We also find that when videos are sped up, students spend less time consuming videos and are marginally more likely to complete more video content. These findings suggest that future study of playback speed as a tool for optimizing video content for MOOCs is warranted. Applications for reinforcement learning and adaptive content are discussed.
{"title":"Is faster better?: a study of video playback speed","authors":"David Lang, Guanling Chen, Kathy Mirzaei, A. Paepcke","doi":"10.1145/3375462.3375466","DOIUrl":"https://doi.org/10.1145/3375462.3375466","url":null,"abstract":"We explore the relationship between video playback speed and student learning outcomes. Using an experimental design, we present the results of a pre-registered study that assigns users to watch videos at either 1.0x or 1.25x speed. We find that students who consume sped content are more likely to get better grades in a course, attempt more content, and obtain more certificates. We also find that when videos are sped up, students spend less time consuming videos and are marginally more likely to complete more video content. These findings suggest that future study of playback speed as a tool for optimizing video content for MOOCs is warranted. Applications for reinforcement learning and adaptive content are discussed.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126200489","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}
We present a data-driven analysis that provides generalized insights of how a curriculum integrated educational math game gets used as a routinized classroom activity throughout the year in authentic primary school classrooms. Our study relates observations from a field study on Spatial Temporal Math (ST Math) to our findings mined from ST Math students' sequential game play data. We identified features that vary across game play sessions and modeled their relationship with session performance. We also derived data-informed suggestions that may provide teachers with insights into how to design classroom game play sessions to facilitate more effective learning.
{"title":"Peeking through the classroom window: a detailed data-driven analysis on the usage of a curriculum integrated math game in authentic classrooms","authors":"Preya Shabrina, Ruth Okoilu Akintunde, Mehak Maniktala, T. Barnes, Collin Lynch, Teomara Rutherford","doi":"10.1145/3375462.3375525","DOIUrl":"https://doi.org/10.1145/3375462.3375525","url":null,"abstract":"We present a data-driven analysis that provides generalized insights of how a curriculum integrated educational math game gets used as a routinized classroom activity throughout the year in authentic primary school classrooms. Our study relates observations from a field study on Spatial Temporal Math (ST Math) to our findings mined from ST Math students' sequential game play data. We identified features that vary across game play sessions and modeled their relationship with session performance. We also derived data-informed suggestions that may provide teachers with insights into how to design classroom game play sessions to facilitate more effective learning.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132048283","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}
Ryan Baker presented in a LAK 2019 keynote a list of six grand challenges for learning analytics research. The challenges are specified as problems with clearly defined success criteria. Education is, however, a domain full of ill-defined problems. I argue that learning analytics research should reflect this nature of the education domain and focus on less clearly defined, but practically essential issues. As an illustration, I discuss three important challenges of this type: addressing inherent trade-offs in learning environments, the clarification of methodological issues, and the scalability of system development.
Ryan Baker在LAK 2019的主题演讲中提出了学习分析研究的六大挑战。挑战被指定为具有明确定义的成功标准的问题。然而，教育是一个充满不明确问题的领域。我认为学习分析研究应该反映教育领域的这一本质，并将重点放在定义不太明确但实际上很重要的问题上。作为一个例子，我讨论了这种类型的三个重要挑战:在学习环境中处理固有的权衡，方法问题的澄清，以及系统开发的可伸缩性。
{"title":"Learning analytics challenges: trade-offs, methodology, scalability","authors":"Radek Pelánek","doi":"10.1145/3375462.3375463","DOIUrl":"https://doi.org/10.1145/3375462.3375463","url":null,"abstract":"Ryan Baker presented in a LAK 2019 keynote a list of six grand challenges for learning analytics research. The challenges are specified as problems with clearly defined success criteria. Education is, however, a domain full of ill-defined problems. I argue that learning analytics research should reflect this nature of the education domain and focus on less clearly defined, but practically essential issues. As an illustration, I discuss three important challenges of this type: addressing inherent trade-offs in learning environments, the clarification of methodological issues, and the scalability of system development.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116265426","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}
Maverick Andre Dionisio Ferreira, V. Rolim, R. F. Mello, R. Lins, Guanliang Chen, D. Gašević
This paper presents an approach to automatic labeling of the content of messages in online discussion according to the categories of social presence. To achieve this goal, the proposed approach is based on a combination of traditional text mining features and word counts extracted with the use of established linguistic frameworks (i.e., LIWC and Coh-metrix). The best performing classifier obtained 0.95 and 0.88 for accuracy and Cohen's kappa, respectively. This paper also provides some theoretical insights into the nature of social presence by looking at the classification features that were most relevant for distinguishing between the different categories. Finally, this study adopted epistemic network analysis to investigate the structural construct validity of the automatic classification approach. Namely, the analysis showed that the epistemic networks produced based on messages manually and automatically coded produced nearly identical results. This finding thus produced evidence of the structural validity of the automatic approach.
{"title":"Towards automatic content analysis of social presence in transcripts of online discussions","authors":"Maverick Andre Dionisio Ferreira, V. Rolim, R. F. Mello, R. Lins, Guanliang Chen, D. Gašević","doi":"10.1145/3375462.3375495","DOIUrl":"https://doi.org/10.1145/3375462.3375495","url":null,"abstract":"This paper presents an approach to automatic labeling of the content of messages in online discussion according to the categories of social presence. To achieve this goal, the proposed approach is based on a combination of traditional text mining features and word counts extracted with the use of established linguistic frameworks (i.e., LIWC and Coh-metrix). The best performing classifier obtained 0.95 and 0.88 for accuracy and Cohen's kappa, respectively. This paper also provides some theoretical insights into the nature of social presence by looking at the classification features that were most relevant for distinguishing between the different categories. Finally, this study adopted epistemic network analysis to investigate the structural construct validity of the automatic classification approach. Namely, the analysis showed that the epistemic networks produced based on messages manually and automatically coded produced nearly identical results. This finding thus produced evidence of the structural validity of the automatic approach.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116461188","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}
Network analysis has become a major approach for analysing social learning, used to capture learner positioning in online forum networks. LA research investigated the association between positioning in forum networks with academic performance and discourse quality, the latter two serving as proxies for learning. However, the research findings have been inconsistent, in part due to the discrepancies in the adopted approaches to network construction. Yet, it is still unclear how online forum networks should be modelled to assure that the learners' network positioning is properly captured. To address this gap, the current study explored if some existing approaches to network construction may complement each other and thus offer richer insights. In particular, we hypothesised that the post-reply learner network could represent interpersonal positioning, whereas the network based on co-participation in discussion threads could encapsulate intergroup positioning. The study used learner social interaction data from a large edX MOOC forum to examine the relationship between these two kinds of network positioning. The results suggest that intergroup and interpersonal positioning may capture different aspects of social learning, potentially related to different learning outcomes. We find that although interpersonal and intergroup positioning indicators covary, these measures are not congruent for some 37% of forum posters. Network coevolution analysis also reveals an interdependent relationship between the intergroup and interpersonal centrality in a forum network. Co-occurrence of learners in a discussion thread prior to direct exchanges is predictive of a direct post-reply interaction at a later stage of the course, and vice-versa, suggesting that intergroup positioning is a precursor of direct communication. The study contributes to the discussion around the definition of learner forum positioning in learning analytics, and validated approaches towards measuring it.
{"title":"Intergroup and interpersonal forum positioning in shared-thread and post-reply networks","authors":"Oleksandra Poquet, J. Jovanović","doi":"10.1145/3375462.3375533","DOIUrl":"https://doi.org/10.1145/3375462.3375533","url":null,"abstract":"Network analysis has become a major approach for analysing social learning, used to capture learner positioning in online forum networks. LA research investigated the association between positioning in forum networks with academic performance and discourse quality, the latter two serving as proxies for learning. However, the research findings have been inconsistent, in part due to the discrepancies in the adopted approaches to network construction. Yet, it is still unclear how online forum networks should be modelled to assure that the learners' network positioning is properly captured. To address this gap, the current study explored if some existing approaches to network construction may complement each other and thus offer richer insights. In particular, we hypothesised that the post-reply learner network could represent interpersonal positioning, whereas the network based on co-participation in discussion threads could encapsulate intergroup positioning. The study used learner social interaction data from a large edX MOOC forum to examine the relationship between these two kinds of network positioning. The results suggest that intergroup and interpersonal positioning may capture different aspects of social learning, potentially related to different learning outcomes. We find that although interpersonal and intergroup positioning indicators covary, these measures are not congruent for some 37% of forum posters. Network coevolution analysis also reveals an interdependent relationship between the intergroup and interpersonal centrality in a forum network. Co-occurrence of learners in a discussion thread prior to direct exchanges is predictive of a direct post-reply interaction at a later stage of the course, and vice-versa, suggesting that intergroup positioning is a precursor of direct communication. The study contributes to the discussion around the definition of learner forum positioning in learning analytics, and validated approaches towards measuring it.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132079674","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}
Online learning environments are now pervasive in higher education. While not exclusively the case, in these environments, there is often modest teacher presence, and students are provided with access to a range of learning, assessment, and support materials. This places pressure on their study skills, including self-regulation. In this context, students may access assessment material without being fully prepared. This may result in limited success and, in turn, raise a significant risk of disengagement. Therefore, if the prediction of students' assessment readiness was possible, it could be used to assist educators or online learning environments to postpone assessment tasks until students were deemed "ready". In this study, we employed a range of machine learning techniques with aggregated and sequential representations of students' behaviour in a Massive Open Online Course (MOOC), to predict their readiness for assessment tasks. Based on our results, it was possible to successfully predict students' readiness for assessment tasks, particularly if the sequential aspects of behaviour were represented in the model. Additionally, we used sequential pattern mining to investigate which sequences of behaviour differed between high or low level of performance in assessments. We found that a high level of performance had the most sequences related to viewing and reviewing the lecture materials, whereas a low level of performance had the most sequences related to successive failed submissions for an assessment. Based on the findings, implications for supporting specific behaviours to improve learning in online environments are discussed.
{"title":"Prediction of students' assessment readiness in online learning environments: the sequence matters","authors":"D. Malekian, J. Bailey, G. Kennedy","doi":"10.1145/3375462.3375468","DOIUrl":"https://doi.org/10.1145/3375462.3375468","url":null,"abstract":"Online learning environments are now pervasive in higher education. While not exclusively the case, in these environments, there is often modest teacher presence, and students are provided with access to a range of learning, assessment, and support materials. This places pressure on their study skills, including self-regulation. In this context, students may access assessment material without being fully prepared. This may result in limited success and, in turn, raise a significant risk of disengagement. Therefore, if the prediction of students' assessment readiness was possible, it could be used to assist educators or online learning environments to postpone assessment tasks until students were deemed \"ready\". In this study, we employed a range of machine learning techniques with aggregated and sequential representations of students' behaviour in a Massive Open Online Course (MOOC), to predict their readiness for assessment tasks. Based on our results, it was possible to successfully predict students' readiness for assessment tasks, particularly if the sequential aspects of behaviour were represented in the model. Additionally, we used sequential pattern mining to investigate which sequences of behaviour differed between high or low level of performance in assessments. We found that a high level of performance had the most sequences related to viewing and reviewing the lecture materials, whereas a low level of performance had the most sequences related to successive failed submissions for an assessment. Based on the findings, implications for supporting specific behaviours to improve learning in online environments are discussed.","PeriodicalId":355800,"journal":{"name":"Proceedings of the Tenth International Conference on Learning Analytics & Knowledge","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130243234","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}
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