Predictive learning analytics (PLA) dashboards have been used by teachers to identify students at risk of failing their studies and provide proactive support. Yet, very few of them have been deployed at a large scale or had their use studied at a mature level of implementation. In this study, we surveyed 366 distance learning university teachers across four faculties three years after PLA has been made available across university as business as usual. Informed by the Unified Theory of Acceptance and Use of Technology (UTAUT), we present a context-specific version of UTAUT that reflects teachers’ perceptions of PLA in distance learning higher education. The adoption and use of PLA was shown to be positively influenced by less experience in teaching, performance expectancy, self-efficacy, positive attitudes, and low anxiety, while negatively influenced by a lack of facilitating conditions and low effort expectancy, indicating that the type of technology and context within which it is used are significant factors determining our understanding of technology usage and adoption. This study provides significant insights as to how to design, apply and implement PLA with teachers in higher education.
{"title":"Predictive Learning Analytics and University Teachers: Usage and perceptions three years post implementation","authors":"C. Maguire, Martin Hlosta, P. Mulholland","doi":"10.1145/3576050.3576061","DOIUrl":"https://doi.org/10.1145/3576050.3576061","url":null,"abstract":"Predictive learning analytics (PLA) dashboards have been used by teachers to identify students at risk of failing their studies and provide proactive support. Yet, very few of them have been deployed at a large scale or had their use studied at a mature level of implementation. In this study, we surveyed 366 distance learning university teachers across four faculties three years after PLA has been made available across university as business as usual. Informed by the Unified Theory of Acceptance and Use of Technology (UTAUT), we present a context-specific version of UTAUT that reflects teachers’ perceptions of PLA in distance learning higher education. The adoption and use of PLA was shown to be positively influenced by less experience in teaching, performance expectancy, self-efficacy, positive attitudes, and low anxiety, while negatively influenced by a lack of facilitating conditions and low effort expectancy, indicating that the type of technology and context within which it is used are significant factors determining our understanding of technology usage and adoption. This study provides significant insights as to how to design, apply and implement PLA with teachers in higher education.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134057740","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}
Kirsty Kitto, Catherine A. Manly, Rebecca Ferguson, Oleksandra Poquet
Content analysis (CA) is a method frequently used in the learning sciences and so increasingly applied in learning analytics (LA). Despite this ubiquity, CA is a subtle method, with many complexities and decision points affecting the outcomes it generates. Although appearing to be a neutral quantitative approach, coding CA constructs requires an attention to decision making and context that aligns it with a more subjective, qualitative interpretation of data. Despite these challenges, we increasingly see the labels in CA-derived datasets used as training sets for machine learning (ML) methods in LA. However, the scarcity of widely shareable datasets means research groups usually work independently to generate labelled data, with few attempts made to compare practice and results across groups. A risk is emerging that different groups are coding constructs in different ways, leading to results that will not prove replicable. We report on two replication studies using a previously reported construct. A failure to achieve high inter-rater reliability suggests that coding of this scheme is not currently replicable across different research groups. We point to potential dangers in this result for those who would use ML to automate the detection of various educationally relevant constructs in LA.
{"title":"Towards more replicable content analysis for learning analytics","authors":"Kirsty Kitto, Catherine A. Manly, Rebecca Ferguson, Oleksandra Poquet","doi":"10.1145/3576050.3576096","DOIUrl":"https://doi.org/10.1145/3576050.3576096","url":null,"abstract":"Content analysis (CA) is a method frequently used in the learning sciences and so increasingly applied in learning analytics (LA). Despite this ubiquity, CA is a subtle method, with many complexities and decision points affecting the outcomes it generates. Although appearing to be a neutral quantitative approach, coding CA constructs requires an attention to decision making and context that aligns it with a more subjective, qualitative interpretation of data. Despite these challenges, we increasingly see the labels in CA-derived datasets used as training sets for machine learning (ML) methods in LA. However, the scarcity of widely shareable datasets means research groups usually work independently to generate labelled data, with few attempts made to compare practice and results across groups. A risk is emerging that different groups are coding constructs in different ways, leading to results that will not prove replicable. We report on two replication studies using a previously reported construct. A failure to achieve high inter-rater reliability suggests that coding of this scheme is not currently replicable across different research groups. We point to potential dangers in this result for those who would use ML to automate the detection of various educationally relevant constructs in LA.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124718713","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}
Learning can be improved by providing personalized feedback adapting to the emotions that the learner may be experiencing. There is initial evidence that co-occurring emotions can be predicted during learning in Intelligent Tutoring Systems (ITS) through eye-tracking and interaction data. Predicting co-occurring emotions is a complex task and merging datasets has the potential to improve predictive performance. In this paper, we combine data from two user studies with an ITS, and analyze whether there is an improvement in predictive performance of co-occurring emotions, despite the user studies using different eye-trackers. In the pursuit towards developing real affect-aware ITS, we look at whether we can isolate classifiers that perform better than a baseline. In this regard we perform a series of statistical analyses and test out the predictive performance of standard machine learning models as well as an ensemble classifier for the task of predicting co-occurring emotions.
{"title":"Predicting Co-occurring Emotions in MetaTutor when Combining Eye-Tracking and Interaction Data from Separate User Studies","authors":"Rohit Murali, C. Conati, R. Azevedo","doi":"10.1145/3576050.3576108","DOIUrl":"https://doi.org/10.1145/3576050.3576108","url":null,"abstract":"Learning can be improved by providing personalized feedback adapting to the emotions that the learner may be experiencing. There is initial evidence that co-occurring emotions can be predicted during learning in Intelligent Tutoring Systems (ITS) through eye-tracking and interaction data. Predicting co-occurring emotions is a complex task and merging datasets has the potential to improve predictive performance. In this paper, we combine data from two user studies with an ITS, and analyze whether there is an improvement in predictive performance of co-occurring emotions, despite the user studies using different eye-trackers. In the pursuit towards developing real affect-aware ITS, we look at whether we can isolate classifiers that perform better than a baseline. In this regard we perform a series of statistical analyses and test out the predictive performance of standard machine learning models as well as an ensemble classifier for the task of predicting co-occurring emotions.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129370558","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}
Riordan Alfredo, Lanbing Nie, Paul Kennedy, T. Power, C. Hayes, Hui Chen, C. McGregor, Z. Swiecki, D. Gašević, Roberto Martínez Maldonado
In recent years, there has been a growing interest in creating multimodal learning analytics (LA) systems that automatically analyse students’ states that are hard to see with the "naked eye", such as cognitive load and stress levels, but that can considerably shape their learning experience. A rich body of research has focused on detecting such aspects by capturing bodily signals from students using wearables and computer vision. Yet, little work has aimed at designing end-user interfaces that visualise physiological data to support tasks deliberately designed for students to learn from stressful situations. This paper addresses this gap by designing a stress analytics dashboard that encodes students’ physiological data into stress levels during different phases of an authentic team simulation in the context of nursing education. We conducted a qualitative study with teachers to understand (i) how they made sense of the stress analytics dashboard; (ii) the extent to which they trusted the dashboard in relation to students’ cortisol data; and (iii) the potential adoption of this tool to communicate insights and aid teaching practices.
{"title":"\"That Student Should be a Lion Tamer!\" StressViz: Designing a Stress Analytics Dashboard for Teachers","authors":"Riordan Alfredo, Lanbing Nie, Paul Kennedy, T. Power, C. Hayes, Hui Chen, C. McGregor, Z. Swiecki, D. Gašević, Roberto Martínez Maldonado","doi":"10.1145/3576050.3576058","DOIUrl":"https://doi.org/10.1145/3576050.3576058","url":null,"abstract":"In recent years, there has been a growing interest in creating multimodal learning analytics (LA) systems that automatically analyse students’ states that are hard to see with the \"naked eye\", such as cognitive load and stress levels, but that can considerably shape their learning experience. A rich body of research has focused on detecting such aspects by capturing bodily signals from students using wearables and computer vision. Yet, little work has aimed at designing end-user interfaces that visualise physiological data to support tasks deliberately designed for students to learn from stressful situations. This paper addresses this gap by designing a stress analytics dashboard that encodes students’ physiological data into stress levels during different phases of an authentic team simulation in the context of nursing education. We conducted a qualitative study with teachers to understand (i) how they made sense of the stress analytics dashboard; (ii) the extent to which they trusted the dashboard in relation to students’ cortisol data; and (iii) the potential adoption of this tool to communicate insights and aid teaching practices.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124550633","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}
Lin Li, Lele Sha, Yuheng Li, Mladen Raković, Jia Rong, Srécko Joksimovíc, N. Selwyn, D. Gašević, Guanliang Chen
Machine Learning (ML) techniques have been increasingly adopted to support various activities in education, including being applied in important contexts such as college admission and scholarship allocation. In addition to being accurate, the application of these techniques has to be fair, i.e., displaying no discrimination towards any group of stakeholders in education (mainly students and instructors) based on their protective attributes (e.g., gender and age). The past few years have witnessed an explosion of attention given to the predictive bias of ML techniques in education. Though certain endeavors have been made to detect and alleviate predictive bias in learning analytics, it is still hard for newcomers to penetrate. To address this, we systematically reviewed existing studies on predictive bias in education, and a total of 49 peer-reviewed empirical papers published after 2010 were included in this study. In particular, these papers were reviewed and summarized from the following three perspectives: (i) protective attributes, (ii) fairness measures and their applications in various educational tasks, and (iii) strategies for enhancing predictive fairness. These findings were summarized into recommendations to guide future endeavors in this strand of research, e.g., collecting and sharing more quality data containing protective attributes, developing fairness-enhancing approaches which do not require the explicit use of protective attributes, validating the effectiveness of fairness-enhancing on students and instructors in real-world settings.
{"title":"Moral Machines or Tyranny of the Majority? A Systematic Review on Predictive Bias in Education","authors":"Lin Li, Lele Sha, Yuheng Li, Mladen Raković, Jia Rong, Srécko Joksimovíc, N. Selwyn, D. Gašević, Guanliang Chen","doi":"10.1145/3576050.3576119","DOIUrl":"https://doi.org/10.1145/3576050.3576119","url":null,"abstract":"Machine Learning (ML) techniques have been increasingly adopted to support various activities in education, including being applied in important contexts such as college admission and scholarship allocation. In addition to being accurate, the application of these techniques has to be fair, i.e., displaying no discrimination towards any group of stakeholders in education (mainly students and instructors) based on their protective attributes (e.g., gender and age). The past few years have witnessed an explosion of attention given to the predictive bias of ML techniques in education. Though certain endeavors have been made to detect and alleviate predictive bias in learning analytics, it is still hard for newcomers to penetrate. To address this, we systematically reviewed existing studies on predictive bias in education, and a total of 49 peer-reviewed empirical papers published after 2010 were included in this study. In particular, these papers were reviewed and summarized from the following three perspectives: (i) protective attributes, (ii) fairness measures and their applications in various educational tasks, and (iii) strategies for enhancing predictive fairness. These findings were summarized into recommendations to guide future endeavors in this strand of research, e.g., collecting and sharing more quality data containing protective attributes, developing fairness-enhancing approaches which do not require the explicit use of protective attributes, validating the effectiveness of fairness-enhancing on students and instructors in real-world settings.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130560922","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}
Rachel Van Campenhout, Bill Jerome, Jeffrey S. Dittel, Benny G. Johnson
The future of digital learning should be focused on methods proven to be effective by learning science and learning analytics. One such method is learning by doing—combining formative practice with expository content so students actively engage with their learning resource. This generates the doer effect: the principle that students who do practice while they read have higher outcomes than those who only read [9]. Research on the doer effect has shown it to be causal to learning [10], and these causal findings have previously been replicated in a single course [19]. This study extends the replication of the doer effect by analyzing 15.2 million data events from 18,546 students in seven courses at an online higher education institution, the most students and courses known to date. Furthermore, we analyze each course five ways by using different outcomes, accounting for prior knowledge, and doing both correlational and causal analyses. By performing the doer effect analyses five ways on seven courses, new insights are gained on how this method of learning analytics can contribute to our interpretation of this learning science principle. Practical implications of the doer effect for students are discussed, and future research goals are established.
{"title":"The Doer Effect at Scale: Investigating Correlation and Causation Across Seven Courses","authors":"Rachel Van Campenhout, Bill Jerome, Jeffrey S. Dittel, Benny G. Johnson","doi":"10.1145/3576050.3576103","DOIUrl":"https://doi.org/10.1145/3576050.3576103","url":null,"abstract":"The future of digital learning should be focused on methods proven to be effective by learning science and learning analytics. One such method is learning by doing—combining formative practice with expository content so students actively engage with their learning resource. This generates the doer effect: the principle that students who do practice while they read have higher outcomes than those who only read [9]. Research on the doer effect has shown it to be causal to learning [10], and these causal findings have previously been replicated in a single course [19]. This study extends the replication of the doer effect by analyzing 15.2 million data events from 18,546 students in seven courses at an online higher education institution, the most students and courses known to date. Furthermore, we analyze each course five ways by using different outcomes, accounting for prior knowledge, and doing both correlational and causal analyses. By performing the doer effect analyses five ways on seven courses, new insights are gained on how this method of learning analytics can contribute to our interpretation of this learning science principle. Practical implications of the doer effect for students are discussed, and future research goals are established.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"51 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115979246","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}
Tanya Nazaretsky, Jamie N. Mikeska, Beata Beigman Klebanov
Engaging students in argument from evidence is an essential goal of science education. This is a complex skill to develop; recent research in science education proposed the use of simulated classrooms to facilitate the practice of the skill. We use data from one such simulated environment to explore whether automated analysis of the transcripts of the teacher’s interaction with the simulated students using Natural Language Processing techniques could yield an accurate evaluation of the teacher’s performance. We are especially interested in explainable models that could also support formative feedback. The results are encouraging: Not only can the models score the transcript as well as humans can, but they can also provide justifications for the scores comparable to those provided by human raters.
{"title":"Empowering Teacher Learning with AI: Automated Evaluation of Teacher Attention to Student Ideas during Argumentation-focused Discussion","authors":"Tanya Nazaretsky, Jamie N. Mikeska, Beata Beigman Klebanov","doi":"10.1145/3576050.3576067","DOIUrl":"https://doi.org/10.1145/3576050.3576067","url":null,"abstract":"Engaging students in argument from evidence is an essential goal of science education. This is a complex skill to develop; recent research in science education proposed the use of simulated classrooms to facilitate the practice of the skill. We use data from one such simulated environment to explore whether automated analysis of the transcripts of the teacher’s interaction with the simulated students using Natural Language Processing techniques could yield an accurate evaluation of the teacher’s performance. We are especially interested in explainable models that could also support formative feedback. The results are encouraging: Not only can the models score the transcript as well as humans can, but they can also provide justifications for the scores comparable to those provided by human raters.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"373 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120971079","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}
The need to personalize learning analytics dashboards (LADs) is getting more recognized in learning analytics research community. In order to study the impact of these dashboards on learners, various types of prototypes have been designed and deployed in different settings. Applying Weiner’s attribution theory, our goal in this study was to understand the effect of dashboard information content on learners. We wanted to understand how elements of assignment grade, time spent on an assignment, assignment view, and proficiency in the dashboard affect students’ attribution of achievement and motivation for future work. We designed a qualitative study in which we analyzed participants’ responses and indicated behavioural changes after viewing the dashboard. Through in-depth interviews, we aimed to understand students’ interpretations of the designed dashboard, and to what extent social comparison impacts their judgments of learning. Students used multiple dimensions to attribute their success or failure to their ability and effort. Our results indicate that to maximize the benefits of dashboards as a vehicle for motivating change in students learning, the dashboard should promote effort in both personal and social comparison capacities.
{"title":"How Students’ Emotion and Motivation Changes After Viewing Dashboards with Varied Social Comparison Group: A Qualitative Study","authors":"Kimia Aghaei, M. Hatala, Alireza Mogharrab","doi":"10.1145/3576050.3576107","DOIUrl":"https://doi.org/10.1145/3576050.3576107","url":null,"abstract":"The need to personalize learning analytics dashboards (LADs) is getting more recognized in learning analytics research community. In order to study the impact of these dashboards on learners, various types of prototypes have been designed and deployed in different settings. Applying Weiner’s attribution theory, our goal in this study was to understand the effect of dashboard information content on learners. We wanted to understand how elements of assignment grade, time spent on an assignment, assignment view, and proficiency in the dashboard affect students’ attribution of achievement and motivation for future work. We designed a qualitative study in which we analyzed participants’ responses and indicated behavioural changes after viewing the dashboard. Through in-depth interviews, we aimed to understand students’ interpretations of the designed dashboard, and to what extent social comparison impacts their judgments of learning. Students used multiple dimensions to attribute their success or failure to their ability and effort. Our results indicate that to maximize the benefits of dashboards as a vehicle for motivating change in students learning, the dashboard should promote effort in both personal and social comparison capacities.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124442625","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}
The term Learner Profile has proliferated over the years, and more recently, with the increased advocacy around personalising learning experiences. Learner profiles are at the center of personalised learning, and the characterisation of diversity in classrooms is made possible by profiling learners based on their strengths and weaknesses, backgrounds and other factors influencing learning. In this paper, we discuss three common approaches of profiling learners based on students’ cognitive knowledge, skills and competencies and behavioral patterns, all latter commonly used within Learning Analytics (LA). Although each approach has its strengths and merits, there are also several disadvantages that have impeded adoption at scale. We propose that the broader adoption of learner profiles can benefit from careful combination of the methods and practices of three primary approaches, allowing for scalable implementation of learner profiles across educational systems. In this regard, LA can leverage from other aligned domains to develop valid and rigorous measures of students' learning and propel learner profiles from education research to more mainstream educational practice. LA could provide the scope for monitoring and reporting beyond an individualised context and allow holistic evaluations of progress. There is promise in LA research to leverage the growing momentum surrounding learner profiles and make a substantial impact on the field's core aim - understanding and optimising learning as it occurs.
{"title":"Advancing leaner profiles with learning analytics: A scoping review of current trends and challenges","authors":"Abhinava Barthakur, S. Dawson, Vitomir Kovanovíc","doi":"10.1145/3576050.3576083","DOIUrl":"https://doi.org/10.1145/3576050.3576083","url":null,"abstract":"The term Learner Profile has proliferated over the years, and more recently, with the increased advocacy around personalising learning experiences. Learner profiles are at the center of personalised learning, and the characterisation of diversity in classrooms is made possible by profiling learners based on their strengths and weaknesses, backgrounds and other factors influencing learning. In this paper, we discuss three common approaches of profiling learners based on students’ cognitive knowledge, skills and competencies and behavioral patterns, all latter commonly used within Learning Analytics (LA). Although each approach has its strengths and merits, there are also several disadvantages that have impeded adoption at scale. We propose that the broader adoption of learner profiles can benefit from careful combination of the methods and practices of three primary approaches, allowing for scalable implementation of learner profiles across educational systems. In this regard, LA can leverage from other aligned domains to develop valid and rigorous measures of students' learning and propel learner profiles from education research to more mainstream educational practice. LA could provide the scope for monitoring and reporting beyond an individualised context and allow holistic evaluations of progress. There is promise in LA research to leverage the growing momentum surrounding learner profiles and make a substantial impact on the field's core aim - understanding and optimising learning as it occurs.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126273494","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}
Learning analytics (LA) has been opening new opportunities to support learning in higher education (HE). LA dashboards are an important tool in providing students with insights into their learning progress, and predictions, leading to reflection and adaptation of learning plans and habits. Based on a human-centered approach, we present a perspective of students, as essential stakeholders, on LA dashboards. We describe a longitudinal study, based on survey methodology. The study included two iterations of a survey, conducted with second-year ICT students in 2017 (N = 222) and 2022 (N = 196). The study provided insights into the LA dashboard features the students find the most useful to support their learning. The students highly appreciated features related to short-term planning and organization of learning, while they were cautious about comparison and competition with other students, finding such features possibly demotivating. We compared the 2017 and 2022 results to establish possible changes in the students’ perspectives with the COVID-19 pandemic. The students’ awareness of the benefits of LA has increased, which may be related to the strong focus on online learning during the pandemic. Finally, a factor analysis yielded a dashboard model with five underlying factors: comparison, planning, predictions, extracurricular, and teachers.
{"title":"Learning analytics dashboards: What do students actually ask for?","authors":"B. Divjak, Barbi Svetec, Damir Horvat","doi":"10.1145/3576050.3576141","DOIUrl":"https://doi.org/10.1145/3576050.3576141","url":null,"abstract":"Learning analytics (LA) has been opening new opportunities to support learning in higher education (HE). LA dashboards are an important tool in providing students with insights into their learning progress, and predictions, leading to reflection and adaptation of learning plans and habits. Based on a human-centered approach, we present a perspective of students, as essential stakeholders, on LA dashboards. We describe a longitudinal study, based on survey methodology. The study included two iterations of a survey, conducted with second-year ICT students in 2017 (N = 222) and 2022 (N = 196). The study provided insights into the LA dashboard features the students find the most useful to support their learning. The students highly appreciated features related to short-term planning and organization of learning, while they were cautious about comparison and competition with other students, finding such features possibly demotivating. We compared the 2017 and 2022 results to establish possible changes in the students’ perspectives with the COVID-19 pandemic. The students’ awareness of the benefits of LA has increased, which may be related to the strong focus on online learning during the pandemic. Finally, a factor analysis yielded a dashboard model with five underlying factors: comparison, planning, predictions, extracurricular, and teachers.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133346594","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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