Pub Date : 2023-11-11DOI: 10.1007/s10758-023-09698-y
Marco Lünich, Birte Keller, Frank Marcinkowski
Abstract Artificial intelligence in higher education is becoming more prevalent as it promises improvements and acceleration of administrative processes concerning student support, aiming for increasing student success and graduation rates. For instance, Academic Performance Prediction (APP) provides individual feedback and serves as the foundation for distributing student support measures. However, the use of APP with all its challenges (e.g., inherent biases) significantly impacts the future prospects of young adults. Therefore, it is important to weigh the opportunities and risks of such systems carefully and involve affected students in the development phase. This study addresses students’ fairness perceptions of the distribution of support measures based on an APP system. First, we examine how students evaluate three different distributive justice norms, namely, equality , equity , and need . Second, we investigate whether fairness perceptions differ between APP based on human or algorithmic decision-making, and third, we address whether evaluations differ between students studying science, technology, engineering, and math (STEM) or social sciences, humanities, and the arts for people and the economy (SHAPE), respectively. To this end, we conducted a cross-sectional survey with a 2 $$times$$ × 3 factorial design among n = 1378 German students, in which we utilized the distinct distribution norms and decision-making agents as design factors. Our findings suggest that students prefer an equality-based distribution of support measures, and this preference is not influenced by whether APP is based on human or algorithmic decision-making. Moreover, the field of study does not influence the fairness perception, except that students of STEM subjects evaluate a distribution based on the need norm as more fair than students of SHAPE subjects. Based on these findings, higher education institutions should prioritize student-centric decisions when considering APP, weigh the actual need against potential risks, and establish continuous feedback through ongoing consultation with all stakeholders.
{"title":"Fairness of Academic Performance Prediction for the Distribution of Support Measures for Students: Differences in Perceived Fairness of Distributive Justice Norms","authors":"Marco Lünich, Birte Keller, Frank Marcinkowski","doi":"10.1007/s10758-023-09698-y","DOIUrl":"https://doi.org/10.1007/s10758-023-09698-y","url":null,"abstract":"Abstract Artificial intelligence in higher education is becoming more prevalent as it promises improvements and acceleration of administrative processes concerning student support, aiming for increasing student success and graduation rates. For instance, Academic Performance Prediction (APP) provides individual feedback and serves as the foundation for distributing student support measures. However, the use of APP with all its challenges (e.g., inherent biases) significantly impacts the future prospects of young adults. Therefore, it is important to weigh the opportunities and risks of such systems carefully and involve affected students in the development phase. This study addresses students’ fairness perceptions of the distribution of support measures based on an APP system. First, we examine how students evaluate three different distributive justice norms, namely, equality , equity , and need . Second, we investigate whether fairness perceptions differ between APP based on human or algorithmic decision-making, and third, we address whether evaluations differ between students studying science, technology, engineering, and math (STEM) or social sciences, humanities, and the arts for people and the economy (SHAPE), respectively. To this end, we conducted a cross-sectional survey with a 2 $$times$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mo>×</mml:mo> </mml:math> 3 factorial design among n = 1378 German students, in which we utilized the distinct distribution norms and decision-making agents as design factors. Our findings suggest that students prefer an equality-based distribution of support measures, and this preference is not influenced by whether APP is based on human or algorithmic decision-making. Moreover, the field of study does not influence the fairness perception, except that students of STEM subjects evaluate a distribution based on the need norm as more fair than students of SHAPE subjects. Based on these findings, higher education institutions should prioritize student-centric decisions when considering APP, weigh the actual need against potential risks, and establish continuous feedback through ongoing consultation with all stakeholders.","PeriodicalId":46366,"journal":{"name":"Technology Knowledge and Learning","volume":"17 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135042884","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}
Pub Date : 2023-11-04DOI: 10.1007/s10758-023-09697-z
Ozan Raşit Yürüm, Tuğba Taşkaya-Temizel, Soner Yıldırım
{"title":"Predictive Video Analytics in Online Courses: A Systematic Literature Review","authors":"Ozan Raşit Yürüm, Tuğba Taşkaya-Temizel, Soner Yıldırım","doi":"10.1007/s10758-023-09697-z","DOIUrl":"https://doi.org/10.1007/s10758-023-09697-z","url":null,"abstract":"","PeriodicalId":46366,"journal":{"name":"Technology Knowledge and Learning","volume":"33 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135774214","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}
Pub Date : 2023-11-02DOI: 10.1007/s10758-023-09694-2
Tomás Alves, Francisco Sousa, Sandra Gama, Joaquim Jorge, Daniel Gonçalves
Abstract Recent research has leveraged peer assessment as a grading system tool where learners are involved in learning and evaluation. However, there is limited knowledge regarding individual differences, such as personality, in peer assessment tasks. We analyze how personality factors affect the peer assessment dynamics of a semester-long remote learning course. Specifically, we investigate how psychological constructs shape how people perceive user-generated content, interact with it, and assess their peers. Our results show that personality traits can predict how effective the peer assessment process will be and the scores and feedback that students provide to their peers. In conclusion, we contribute design guidelines based on personality constructs as valuable factors to include in the design pipeline of peer assessment systems.
{"title":"How Personality Traits Affect Peer Assessment in Distance Learning","authors":"Tomás Alves, Francisco Sousa, Sandra Gama, Joaquim Jorge, Daniel Gonçalves","doi":"10.1007/s10758-023-09694-2","DOIUrl":"https://doi.org/10.1007/s10758-023-09694-2","url":null,"abstract":"Abstract Recent research has leveraged peer assessment as a grading system tool where learners are involved in learning and evaluation. However, there is limited knowledge regarding individual differences, such as personality, in peer assessment tasks. We analyze how personality factors affect the peer assessment dynamics of a semester-long remote learning course. Specifically, we investigate how psychological constructs shape how people perceive user-generated content, interact with it, and assess their peers. Our results show that personality traits can predict how effective the peer assessment process will be and the scores and feedback that students provide to their peers. In conclusion, we contribute design guidelines based on personality constructs as valuable factors to include in the design pipeline of peer assessment systems.","PeriodicalId":46366,"journal":{"name":"Technology Knowledge and Learning","volume":"55 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135972719","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}
Pub Date : 2023-11-02DOI: 10.1007/s10758-023-09702-5
Hope K. Gerde, Gary E. Bingham
{"title":"Teachers’ Beliefs and Usage of Video Exemplars and Engagement Features of an Online Professional Learning System for Promoting Early Writing","authors":"Hope K. Gerde, Gary E. Bingham","doi":"10.1007/s10758-023-09702-5","DOIUrl":"https://doi.org/10.1007/s10758-023-09702-5","url":null,"abstract":"","PeriodicalId":46366,"journal":{"name":"Technology Knowledge and Learning","volume":"39 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934617","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}
Pub Date : 2023-10-28DOI: 10.1007/s10758-023-09696-0
Kacee Lambright
{"title":"The Effect of a Teacher’s Mindset on the Cascading Zones of Proximal Development: A Systematic Review","authors":"Kacee Lambright","doi":"10.1007/s10758-023-09696-0","DOIUrl":"https://doi.org/10.1007/s10758-023-09696-0","url":null,"abstract":"","PeriodicalId":46366,"journal":{"name":"Technology Knowledge and Learning","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136160257","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}
Pub Date : 2023-10-27DOI: 10.1007/s10758-023-09699-x
Jennifer G. Cromley, Runzhi Chen
{"title":"Instructional Support for Visual Displays: An Updated Literature Review","authors":"Jennifer G. Cromley, Runzhi Chen","doi":"10.1007/s10758-023-09699-x","DOIUrl":"https://doi.org/10.1007/s10758-023-09699-x","url":null,"abstract":"","PeriodicalId":46366,"journal":{"name":"Technology Knowledge and Learning","volume":"27 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136262610","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}
Pub Date : 2023-10-10DOI: 10.1007/s10758-023-09693-3
Shuai Zhang, Kausalai Kay Wijekumar
{"title":"Teacher Professional Development and Student Reading Comprehension Outcomes: The Heterogeneity of Responsiveness to Text Structure Instruction in Grade 2","authors":"Shuai Zhang, Kausalai Kay Wijekumar","doi":"10.1007/s10758-023-09693-3","DOIUrl":"https://doi.org/10.1007/s10758-023-09693-3","url":null,"abstract":"","PeriodicalId":46366,"journal":{"name":"Technology Knowledge and Learning","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136296203","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}
Pub Date : 2023-10-09DOI: 10.1007/s10758-023-09692-4
Nikola Balić, Ani Grubišić, Andrina Granić
{"title":"Perceptions of Digital Learning and Teaching: The Case of a Croatian University Transition to an Emergency Digital Environment","authors":"Nikola Balić, Ani Grubišić, Andrina Granić","doi":"10.1007/s10758-023-09692-4","DOIUrl":"https://doi.org/10.1007/s10758-023-09692-4","url":null,"abstract":"","PeriodicalId":46366,"journal":{"name":"Technology Knowledge and Learning","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135094150","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}
Pub Date : 2023-09-25DOI: 10.1007/s10758-023-09683-5
Jyothish Asokkumar, Kannan Sekar, Angela Susan Mathew, Ronny Thomas
{"title":"Intention Among Information Technology Professionals to Adopt Paid MOOCs from E-Learning Platforms: An Empirical Study","authors":"Jyothish Asokkumar, Kannan Sekar, Angela Susan Mathew, Ronny Thomas","doi":"10.1007/s10758-023-09683-5","DOIUrl":"https://doi.org/10.1007/s10758-023-09683-5","url":null,"abstract":"","PeriodicalId":46366,"journal":{"name":"Technology Knowledge and Learning","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135817481","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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