The notion of Human-Centred Learning Analytics (HCLA) is gaining traction as educators and learning analytics (LA) researchers recognise the need to align analytics and artificial intelligence (AI) technologies with specific educational contexts. This has led an increasing number of researchers to adopt approaches, such as co-design and participatory design, to include educators and students as active participants in the LA design process. However, some experts contend that HCLA must go beyond stakeholder participation by also focusing on the safety, reliability, and trustworthiness of the analytics, and balancing human control and algorithmic automation. While the adoption of human-centred design (HCD) approaches promises considerable benefits, implementing these practices in data-intensive educational systems may not be straightforward. This paper emphasises the critical need to address specific ethical, technical, and methodological challenges tied to educational and data contexts, in order to effectively apply HCD in the creation of LA systems. We delve into four key challenges in this context: i) ensuring representative participation; ii) considering expertise and lived experiences in LA design; iii) balancing stakeholder input with technological innovation; and iv) navigating power dynamics and decision-making processes. LIFT Learning: Engage further with the author and the challenges faced when adopting human-centered approaches in learning analytics at the companion LIFT Learning site. The author will be hosting a live webinar on Tuesday 12 September 2023 at 6-7pm AEST (8-9am UTC). Visit the LIFT Learning site at https://apps.lift.c3l.ai/learning/course/coursev1:LEARNINGLETTERS+0106+2023 to sign up for your free ticket to this event. If you are unable to attend the webinar live, then the recording will be made available on this same site shortly afterwards.
{"title":"Human-centred learning analytics: Four challenges in realising the potential","authors":"Roberto Martínez-Maldonado","doi":"10.59453/fizj7007","DOIUrl":"https://doi.org/10.59453/fizj7007","url":null,"abstract":"The notion of Human-Centred Learning Analytics (HCLA) is gaining traction as educators and learning analytics (LA) researchers recognise the need to align analytics and artificial intelligence (AI) technologies with specific educational contexts. This has led an increasing number of researchers to adopt approaches, such as co-design and participatory design, to include educators and students as active participants in the LA design process. However, some experts contend that HCLA must go beyond stakeholder participation by also focusing on the safety, reliability, and trustworthiness of the analytics, and balancing human control and algorithmic automation. While the adoption of human-centred design (HCD) approaches promises considerable benefits, implementing these practices in data-intensive educational systems may not be straightforward. This paper emphasises the critical need to address specific ethical, technical, and methodological challenges tied to educational and data contexts, in order to effectively apply HCD in the creation of LA systems. We delve into four key challenges in this context: i) ensuring representative participation; ii) considering expertise and lived experiences in LA design; iii) balancing stakeholder input with technological innovation; and iv) navigating power dynamics and decision-making processes. LIFT Learning: Engage further with the author and the challenges faced when adopting human-centered approaches in learning analytics at the companion LIFT Learning site. The author will be hosting a live webinar on Tuesday 12 September 2023 at 6-7pm AEST (8-9am UTC). Visit the LIFT Learning site at https://apps.lift.c3l.ai/learning/course/coursev1:LEARNINGLETTERS+0106+2023 to sign up for your free ticket to this event. If you are unable to attend the webinar live, then the recording will be made available on this same site shortly afterwards.","PeriodicalId":430337,"journal":{"name":"Journal of Learning Letters","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124035772","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}
Chen Zhan, Oscar Blessed Deho, Xuwei Zhang, Srécko Joksimovíc, M. de Laat
The ongoing digital transformation in the education sector has led to an increased focus on the adoption of Learning Analytics (LA) techniques. LA collects and uses students’ data to gain insights about students’ learning and guide interventions and feedback. Despite a great potential for improving teaching and learning, the use of LA has also raised important questions about the privacy and ethical implications of collecting and using student data. Despite recent efforts to tackle these challenges through the implementation of privacy-preserving approaches and the proposal of ethical guidelines and policies, there remains an insufficiency in ensuring the full protection of student privacy and well-being. Therefore, as a solution to privacy and ethical concerns in LA, there is a high demand for synthetic data generators that can learn from realistic data to generate synthetic data that closely resembles the original data. This paper aims to examine existing synthetic data generators from the broader community in terms of their performances with student data, as well as the capabilities of serving LA models. A comparative study is conducted by applying a set of different synthetic data generators in Synthetic Data Vault (SDV), an open-sourced synthetic data generation ecosystem of libraries, to real-world student data from a university. We report the efficiencies of different generators and the qualities of generated synthetic datasets regarding their statistical properties against realistic data. Furthermore, we test the compatibility between synthetic data generators and LA models by fitting generated synthetic datasets into common-used LA models. By aligning with the ground truth (realistic data), we evaluated the performances of LA models trained by synthetic datasets as indicators of their capability of serving LA models.
{"title":"Synthetic Data Generator for Student Data Serving Learning Analytics: A Comparative Study","authors":"Chen Zhan, Oscar Blessed Deho, Xuwei Zhang, Srécko Joksimovíc, M. de Laat","doi":"10.59453//khzw9006","DOIUrl":"https://doi.org/10.59453//khzw9006","url":null,"abstract":"The ongoing digital transformation in the education sector has led to an increased focus on the adoption of Learning Analytics (LA) techniques. LA collects and uses students’ data to gain insights about students’ learning and guide interventions and feedback. Despite a great potential for improving teaching and learning, the use of LA has also raised important questions about the privacy and ethical implications of collecting and using student data. Despite recent efforts to tackle these challenges through the implementation of privacy-preserving approaches and the proposal of ethical guidelines and policies, there remains an insufficiency in ensuring the full protection of student privacy and well-being. Therefore, as a solution to privacy and ethical concerns in LA, there is a high demand for synthetic data generators that can learn from realistic data to generate synthetic data that closely resembles the original data. This paper aims to examine existing synthetic data generators from the broader community in terms of their performances with student data, as well as the capabilities of serving LA models. A comparative study is conducted by applying a set of different synthetic data generators in Synthetic Data Vault (SDV), an open-sourced synthetic data generation ecosystem of libraries, to real-world student data from a university. We report the efficiencies of different generators and the qualities of generated synthetic datasets regarding their statistical properties against realistic data. Furthermore, we test the compatibility between synthetic data generators and LA models by fitting generated synthetic datasets into common-used LA models. By aligning with the ground truth (realistic data), we evaluated the performances of LA models trained by synthetic datasets as indicators of their capability of serving LA models.","PeriodicalId":430337,"journal":{"name":"Journal of Learning Letters","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130366196","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}
A. Littlejohn, Koula Charitonos, Fereshte Goshtasbpour, S. Dawadi, R. McMullan
Digital Technologies open opportunities to work and learn in new ways as work environments and practices are transformed. To keep pace with these transformations, professionals have to learn continuously, amplifying demand for professional learning. Learning supported by digital systems, AI and analytics offers benefits in terms of access, scale and scaffolded support for learning. However, the learning often is separated from the context of the work environment, making it difficult for professionals to learn and then apply the knowledge learned to their work. To address his problem we question: How can the design of digital professional learning be connected to authentic workplace problems in-context? We propose a novel method - Workplace Logic Model - that brings together knowledge of various stakeholders to pinpoint workplace problems and negotiate actions to address these issues. The Workplace Logic Model method provides a way to bridge the persistent gap between workplace problems and learning actions.
{"title":"The Workplace Logic Model: A method to address authentic work problems through workplace learning actions","authors":"A. Littlejohn, Koula Charitonos, Fereshte Goshtasbpour, S. Dawadi, R. McMullan","doi":"10.59453/eyev8004","DOIUrl":"https://doi.org/10.59453/eyev8004","url":null,"abstract":"Digital Technologies open opportunities to work and learn in new ways as work environments and practices are transformed. To keep pace with these transformations, professionals have to learn continuously, amplifying demand for professional learning. Learning supported by digital systems, AI and analytics offers benefits in terms of access, scale and scaffolded support for learning. However, the learning often is separated from the context of the work environment, making it difficult for professionals to learn and then apply the knowledge learned to their work. To address his problem we question: How can the design of digital professional learning be connected to authentic workplace problems in-context? We propose a novel method - Workplace Logic Model - that brings together knowledge of various stakeholders to pinpoint workplace problems and negotiate actions to address these issues. The Workplace Logic Model method provides a way to bridge the persistent gap between workplace problems and learning actions.","PeriodicalId":430337,"journal":{"name":"Journal of Learning Letters","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123827918","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}
George Siemens, M. de Laat, Florence Gabriel, Negin Mirriahi
Lift Learning: Hear more about Learning Letters from the Editors-in-Chief on the LIFT Learning site. In their webcast they discuss the needs for a new research journal in order to keep pace with developments in educational technology in education. Their webcast is available at https://lift.c3l.ai/courses/course-v1:LEARNINGLETTERS+0100+2023
{"title":"Learning Letters: Why a new journal?","authors":"George Siemens, M. de Laat, Florence Gabriel, Negin Mirriahi","doi":"10.59453/nlsl7954","DOIUrl":"https://doi.org/10.59453/nlsl7954","url":null,"abstract":"Lift Learning: Hear more about Learning Letters from the Editors-in-Chief on the LIFT Learning site. In their webcast they discuss the needs for a new research journal in order to keep pace with developments in educational technology in education. Their webcast is available at https://lift.c3l.ai/courses/course-v1:LEARNINGLETTERS+0100+2023","PeriodicalId":430337,"journal":{"name":"Journal of Learning Letters","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129691369","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 use of AI-powered educational technologies (AI-EdTech) offers a range of advantages to students, instructors, and educational institutions. While much has been achieved, several challenges in managing the data underpinning AI-EdTech are limiting progress in the field. This paper outlines some of these challenges and argues that data management research has the potential to provide solutions that can enable responsible and effective learner-supporting, teacher-supporting, and institution-supporting AI-EdTech. Our hope is to establish a common ground for collaboration and to foster partnerships among educational experts, AI developers and data management researchers to effectively respond to the rapidly evolving global educational landscape and drive the development of AI-EdTech.
{"title":"Data management of AI-powered education technologies: Challenges and opportunities","authors":"Hassan Khosravi, Shazia Sadiq, S. Amer-Yahia","doi":"10.59453//xlud7002","DOIUrl":"https://doi.org/10.59453//xlud7002","url":null,"abstract":"The use of AI-powered educational technologies (AI-EdTech) offers a range of advantages to students, instructors, and educational institutions. While much has been achieved, several challenges in managing the data underpinning AI-EdTech are limiting progress in the field. This paper outlines some of these challenges and argues that data management research has the potential to provide solutions that can enable responsible and effective learner-supporting, teacher-supporting, and institution-supporting AI-EdTech. Our hope is to establish a common ground for collaboration and to foster partnerships among educational experts, AI developers and data management researchers to effectively respond to the rapidly evolving global educational landscape and drive the development of AI-EdTech.","PeriodicalId":430337,"journal":{"name":"Journal of Learning Letters","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121811819","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 notion of Human-Centered Learning Analytics (HCLA) is gaining traction as educators and learning analytics (LA) researchers recognise the need to align artificial intelligence (AI) technologies with particular educational contexts. This has led an increasing number of researchers to adopt participatory approaches, such as co-design and participatory design, to involve educators and students in the design and development of LA systems. However, some experts contend that HCLA must go beyond stakeholder involvement and prioritize safety, reliability, trustworthiness, and finding a balance between human control and AI automation. Drawing from recent research in the field and the author’s first-hand experiences in conducting HCLA research, this paper discusses opportunities and challenges associated with HCLA. This paper highlights that the adoption of human-centred design approaches can help develop LA systems that align with pedagogical intentions by enabling dialogue among stakeholders and leveraging their expertise and lived experiences. The paper also highlights the crucial need to address ethical, technical, and methodological challenges specific to educational and data contexts for effectively applying human-centred design in the development of learning analytics systems. The paper concludes with recommendations for future research and practice, emphasising the importance of defining the scope of HCLA and continuing collaboration among LA researchers, practitioners, learning scientists, and educational stakeholders to advance the development of HCLA to support meaningful and effective learning experiences.
{"title":"Human-Centred Learning Analytics: Opportunities and Challenges","authors":"Roberto Martínez-Maldonado","doi":"10.59453//fizj7007","DOIUrl":"https://doi.org/10.59453//fizj7007","url":null,"abstract":"The notion of Human-Centered Learning Analytics (HCLA) is gaining traction as educators and learning analytics (LA) researchers recognise the need to align artificial intelligence (AI) technologies with particular educational contexts. This has led an increasing number of researchers to adopt participatory approaches, such as co-design and participatory design, to involve educators and students in the design and development of LA systems. However, some experts contend that HCLA must go beyond stakeholder involvement and prioritize safety, reliability, trustworthiness, and finding a balance between human control and AI automation. Drawing from recent research in the field and the author’s first-hand experiences in conducting HCLA research, this paper discusses opportunities and challenges associated with HCLA. This paper highlights that the adoption of human-centred design approaches can help develop LA systems that align with pedagogical intentions by enabling dialogue among stakeholders and leveraging their expertise and lived experiences. The paper also highlights the crucial need to address ethical, technical, and methodological challenges specific to educational and data contexts for effectively applying human-centred design in the development of learning analytics systems. The paper concludes with recommendations for future research and practice, emphasising the importance of defining the scope of HCLA and continuing collaboration among LA researchers, practitioners, learning scientists, and educational stakeholders to advance the development of HCLA to support meaningful and effective learning experiences.","PeriodicalId":430337,"journal":{"name":"Journal of Learning Letters","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124901397","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}
Lisa-Angelique Lim, S. Buckingham Shum, P. Felten, Jennifer Uno
It is well-established that a student’s sense of belonging is associated with successful transition into higher education, along with a raft of positive outcomes including enhanced learning, well-being, and attainment for all students. The importance of belonging was further heightened by the Covid-19 pandemic, as the increased shift to online learning highlighted the challenges of monitoring and supporting student belonging in online settings. A significant challenge lies in the contested nature of belonging, as well as its complexity – students’ experience of belonging is both dynamic and contextual. In creating a new agenda connecting the fields of belonging and learning analytics, we propose the idea of “belonging analytics” to address the challenge of tracking students’ belonging. We present the emerging landscape of belonging by discussing how the advancements in the learning analytics field indicate great potential for the field to explore how digital trace data, narratives, textual data, or a combination, could be harnessed to gain insights into the ongoing experience of belonging, and consequently, to support belonging. We conclude with a set of open questions to interested researchers, to advance the field of belonging analytics.
{"title":"“Belonging Analytics”: A Proposal","authors":"Lisa-Angelique Lim, S. Buckingham Shum, P. Felten, Jennifer Uno","doi":"10.59453//eaxa8005","DOIUrl":"https://doi.org/10.59453//eaxa8005","url":null,"abstract":"It is well-established that a student’s sense of belonging is associated with successful transition into higher education, along with a raft of positive outcomes including enhanced learning, well-being, and attainment for all students. The importance of belonging was further heightened by the Covid-19 pandemic, as the increased shift to online learning highlighted the challenges of monitoring and supporting student belonging in online settings. A significant challenge lies in the contested nature of belonging, as well as its complexity – students’ experience of belonging is both dynamic and contextual. In creating a new agenda connecting the fields of belonging and learning analytics, we propose the idea of “belonging analytics” to address the challenge of tracking students’ belonging. We present the emerging landscape of belonging by discussing how the advancements in the learning analytics field indicate great potential for the field to explore how digital trace data, narratives, textual data, or a combination, could be harnessed to gain insights into the ongoing experience of belonging, and consequently, to support belonging. We conclude with a set of open questions to interested researchers, to advance the field of belonging analytics.","PeriodicalId":430337,"journal":{"name":"Journal of Learning Letters","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123484863","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}
S. Fowler, Malgorzata Korolkiewicz, Rebecca Marrone
Based on the experience of the first 100 days of ChatGPT integration, the article discusses the potential impact of large language models (LLMs) on the Australian university sector. Using a content analysis of websites and quotes from university spokespeople in the media, the authors note that despite the potential benefits of LLMs in transforming teaching and learning, early media coverage has mainly focused on obstacles to adoption. The authors argue that the lack of official recommendations for Artificial Intelligence (AI) implementation has further impeded progress. Several recommendations for successful AI integration in higher education are proposed to address these challenges. These include developing a clear AI strategy that aligns with institutional goals, investing in infrastructure and staff training, and establishing guidelines for the ethical and transparent use of AI. The importance of involving all stakeholders in the decision-making process to ensure successful adoption is also stressed. The article offers valuable insights for policymakers and university leaders interested in harnessing the potential of AI to improve the quality of education and enhance the student experience.
{"title":"First 100 days of ChatGPT at Australian Universities An analysis of policy landscape and media discussions about the role of AI in Higher Education","authors":"S. Fowler, Malgorzata Korolkiewicz, Rebecca Marrone","doi":"10.59453/jmtn6001","DOIUrl":"https://doi.org/10.59453/jmtn6001","url":null,"abstract":"Based on the experience of the first 100 days of ChatGPT integration, the article discusses the potential impact of large language models (LLMs) on the Australian university sector. Using a content analysis of websites and quotes from university spokespeople in the media, the authors note that despite the potential benefits of LLMs in transforming teaching and learning, early media coverage has mainly focused on obstacles to adoption. The authors argue that the lack of official recommendations for Artificial Intelligence (AI) implementation has further impeded progress. Several recommendations for successful AI integration in higher education are proposed to address these challenges. These include developing a clear AI strategy that aligns with institutional goals, investing in infrastructure and staff training, and establishing guidelines for the ethical and transparent use of AI. The importance of involving all stakeholders in the decision-making process to ensure successful adoption is also stressed. The article offers valuable insights for policymakers and university leaders interested in harnessing the potential of AI to improve the quality of education and enhance the student experience.","PeriodicalId":430337,"journal":{"name":"Journal of Learning Letters","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127704644","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}
Recent advancements in assessment analytics provide potential to support learning processes and provide them with relevant informative feedback when needed. Yet, few well-defined indications yield valid data points for assessment analytics. The categorization of indicators that is presented here is designed to provide insights into the possible approaches to assessment and the meaningful connection to assessment analytics. Ethics, social responsibility, privacy, and data protection must be fully respected when following the categorization of indicators for assessment analytics.
{"title":"Toward a Categorization of Indicators for Assessment Analytics","authors":"Dirk Ifenthaler, Joana Heil, Samuel Greiff","doi":"10.59453/cctb2003","DOIUrl":"https://doi.org/10.59453/cctb2003","url":null,"abstract":"Recent advancements in assessment analytics provide potential to support learning processes and provide them with relevant informative feedback when needed. Yet, few well-defined indications yield valid data points for assessment analytics. The categorization of indicators that is presented here is designed to provide insights into the possible approaches to assessment and the meaningful connection to assessment analytics. Ethics, social responsibility, privacy, and data protection must be fully respected when following the categorization of indicators for assessment analytics.","PeriodicalId":430337,"journal":{"name":"Journal of Learning Letters","volume":"8 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131699398","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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