The quality of student learning and academic rigor is central to higher education. Nonetheless, colleges often prioritize metrics such as enrollment and graduation rates or use assessment data to solely fulfill accreditation requirements. The Academic Quality Assurance (AQA) team at a university ventured to expand the academic quality data landscape to learn more about student achievement. The paper shares the team’s journey to collect and report on student performance data for continuous improvement of academic programs. Specifically, this paper includes the methods to promote a culture of assessment by incorporating new concepts into the AQA process: Data visualization and storytelling with data. This paper includes the methodology to collect and report on data, samples of the systems and visualizations used, and the challenges faced in the context of people, process, and tools.
{"title":"Establishing a Sustainable Process to Measure Learner Performance","authors":"M. Saxena, Melanie Kasparian","doi":"10.3991/ijai.v2i1.13083","DOIUrl":"https://doi.org/10.3991/ijai.v2i1.13083","url":null,"abstract":"The quality of student learning and academic rigor is central to higher education. Nonetheless, colleges often prioritize metrics such as enrollment and graduation rates or use assessment data to solely fulfill accreditation requirements. The Academic Quality Assurance (AQA) team at a university ventured to expand the academic quality data landscape to learn more about student achievement. The paper shares the team’s journey to collect and report on student performance data for continuous improvement of academic programs. Specifically, this paper includes the methods to promote a culture of assessment by incorporating new concepts into the AQA process: Data visualization and storytelling with data. This paper includes the methodology to collect and report on data, samples of the systems and visualizations used, and the challenges faced in the context of people, process, and tools.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133463316","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 digital media is increasingly being promoted in school teaching. Since this aspect changes the interaction between teachers and pupils, this research is concerned with the development of a prototype of a mobile application for Android and iOS, in which different learning applications for language acquisition are offered on the basis of learning analytical measurements provided by experts in the field. By logging and collecting interactions of the user, it is possible to create a variety of statistical evaluations and thus respond to the needs and weaknesses of students. For the evaluation of the application, a user experience test was carried out, whereby the child-friendly operation of the application was tested. Due to the very positive feedback, the design was found to be good and can therefore be further developed.
{"title":"Mobile Learning Applications for Android und iOS for German Language Acquisition based on Learning Analytics Measurements","authors":"Markus Friedl, Markus Ebner, Martin Ebner","doi":"10.3991/ijai.v2i1.12317","DOIUrl":"https://doi.org/10.3991/ijai.v2i1.12317","url":null,"abstract":"The use of digital media is increasingly being promoted in school teaching. Since this aspect changes the interaction between teachers and pupils, this research is concerned with the development of a prototype of a mobile application for Android and iOS, in which different learning applications for language acquisition are offered on the basis of learning analytical measurements provided by experts in the field. By logging and collecting interactions of the user, it is possible to create a variety of statistical evaluations and thus respond to the needs and weaknesses of students. For the evaluation of the application, a user experience test was carried out, whereby the child-friendly operation of the application was tested. Due to the very positive feedback, the design was found to be good and can therefore be further developed.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124629886","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 data-driven development of education through Learning Analytics in combination with Artificial Intelligence is an emerging field in the education sector. In the field of Artificial Intelligence in Education, numerous studies and research have been carried out over the past 60 years, and since then drastic changes have taken place. In the first part of this paper we present a brief overview of the current status of Learning Analytics and Artificial Intelligence in education. In order to develop a better understanding of the relationship between Learning Analytics and Artificial Intelligence in education, we outline the relationship between the two phenomena. The results show that the previous studies only vaguely distinguish between them: the terms are often used synonymously. In the second part of the paper we focus on the question why the European market currently has hardly any real applications for Artificial Intelligence in education. The research is based on a meta-investigation of data-driven business models, in particular the so-called Educational Technology providers. The core of the analysis is the question of how data-driven these companies really are, how much Learning Analytics and Artificial Intelligence is applied and whether there is a causal connection between the growth of the Educational Technology market and the application relevance of Artificial Intelligence in Education. In the scientific and public discourse, we can observe a distortion between the theoretical-conjunctive understanding of the application of Artificial Intelligence in Education and the current practical relevance.
{"title":"Demystification of Artificial Intelligence in Education - How much AI is really in the Educational Technology?","authors":"André Renz, Swathi Krishnaraja, E. Gronau","doi":"10.3991/ijai.v2i1.12675","DOIUrl":"https://doi.org/10.3991/ijai.v2i1.12675","url":null,"abstract":"The data-driven development of education through Learning Analytics in combination with Artificial Intelligence is an emerging field in the education sector. In the field of Artificial Intelligence in Education, numerous studies and research have been carried out over the past 60 years, and since then drastic changes have taken place. In the first part of this paper we present a brief overview of the current status of Learning Analytics and Artificial Intelligence in education. In order to develop a better understanding of the relationship between Learning Analytics and Artificial Intelligence in education, we outline the relationship between the two phenomena. The results show that the previous studies only vaguely distinguish between them: the terms are often used synonymously. In the second part of the paper we focus on the question why the European market currently has hardly any real applications for Artificial Intelligence in education. The research is based on a meta-investigation of data-driven business models, in particular the so-called Educational Technology providers. The core of the analysis is the question of how data-driven these companies really are, how much Learning Analytics and Artificial Intelligence is applied and whether there is a causal connection between the growth of the Educational Technology market and the application relevance of Artificial Intelligence in Education. In the scientific and public discourse, we can observe a distortion between the theoretical-conjunctive understanding of the application of Artificial Intelligence in Education and the current practical relevance.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130201293","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}
Chatbots are already being used successfully in many areas. This publication deals with the development and programming of a chatbot prototype to support learning processes. This Chatbot prototype is designed to help pupils in order to correct their spelling mistakes by providing correction proposals to them. Especially orthographic spelling mistake should be recognized by the chatbot and should be replaced by correction suggestions stored in test data.
{"title":"Potentials of Chatbots for Spell Check among Youngsters","authors":"Jeton Arifi, Markus Ebner, Martin Ebner","doi":"10.3991/IJAI.V1I1.10999","DOIUrl":"https://doi.org/10.3991/IJAI.V1I1.10999","url":null,"abstract":"Chatbots are already being used successfully in many areas. This publication deals with the development and programming of a chatbot prototype to support learning processes. This Chatbot prototype is designed to help pupils in order to correct their spelling mistakes by providing correction proposals to them. Especially orthographic spelling mistake should be recognized by the chatbot and should be replaced by correction suggestions stored in test data.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120956564","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}
In this editorial, the first issue of the International Journal of Learning Analytics and Artificial Intelligence for Education is presented. The Journal of Learning Analytics and Artificial Intelligence for Education is a peer-reviewed, open access journal that aim to disseminate highest quality research in the field. The journal aims to increase knowledge and understanding of ways in which learning analytics and artificial intelligence can support and enhance education. The editorial presents the scope and fields of interest for the journal, and an overview of the articles published in the first issue.
{"title":"Editorial of the First Issue of the International Journal of Learning Analytics and Artificial Intelligence for Education","authors":"Jalal Nouri","doi":"10.3991/IJAI.V1I1.11073","DOIUrl":"https://doi.org/10.3991/IJAI.V1I1.11073","url":null,"abstract":"In this editorial, the first issue of the International Journal of Learning Analytics and Artificial Intelligence for Education is presented. The Journal of Learning Analytics and Artificial Intelligence for Education is a peer-reviewed, open access journal that aim to disseminate highest quality research in the field. The journal aims to increase knowledge and understanding of ways in which learning analytics and artificial intelligence can support and enhance education. The editorial presents the scope and fields of interest for the journal, and an overview of the articles published in the first issue.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121355237","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}
Jalal Nouri, Martin Ebner, Dirk Ifenthaler, Mohammed Saqr, Jonna Malmberg, Mohammad Khalil, J. Bruun, Olga Viberg, Miguel Ángel Conde González, Z. Papamitsiou, U. D. Berthelsen
Information and communication technologies are increasingly mediating learning and teaching practices as well as how educational institutions are handling their administrative work. As such, students and teachers are leaving large amounts of digital footprints and traces in various educational apps and learning management platforms, and educational administrators register various processes and outcomes in digital administrative systems. It is against such a background we in recent years have seen the emergence of the fast-growing and multi-disciplinary field of learning analytics. In this paper, we examine the research efforts that have been conducted in the field of learning analytics in Austria, Denmark, Finland, Norway, Germany, Spain, and Sweden. More specifically, we report on developed national policies, infrastructures and competence centers, as well as major research projects and developed research strands within the selected countries. The main conclusions of this paper are that the work of researchers around Europe has not led to national adoption or European level strategies for learning analytics. Furthermore, most countries have not established national policies for learners’ data or guidelines that govern the ethical usage of data in research or education. We also conclude, that learning analytics research on pre-university level to high extent have been overlooked. In the same vein, learning analytics has not received enough focus form national and European national bodies. Such funding is necessary for taking steps towards data-driven development of education.
{"title":"Efforts in Europe for Data-Driven Improvement of Education - A Review of Learning Analytics Research in Seven Countries","authors":"Jalal Nouri, Martin Ebner, Dirk Ifenthaler, Mohammed Saqr, Jonna Malmberg, Mohammad Khalil, J. Bruun, Olga Viberg, Miguel Ángel Conde González, Z. Papamitsiou, U. D. Berthelsen","doi":"10.3991/IJAI.V1I1.11053","DOIUrl":"https://doi.org/10.3991/IJAI.V1I1.11053","url":null,"abstract":"Information and communication technologies are increasingly mediating learning and teaching practices as well as how educational institutions are handling their administrative work. As such, students and teachers are leaving large amounts of digital footprints and traces in various educational apps and learning management platforms, and educational administrators register various processes and outcomes in digital administrative systems. It is against such a background we in recent years have seen the emergence of the fast-growing and multi-disciplinary field of learning analytics. In this paper, we examine the research efforts that have been conducted in the field of learning analytics in Austria, Denmark, Finland, Norway, Germany, Spain, and Sweden. More specifically, we report on developed national policies, infrastructures and competence centers, as well as major research projects and developed research strands within the selected countries. The main conclusions of this paper are that the work of researchers around Europe has not led to national adoption or European level strategies for learning analytics. Furthermore, most countries have not established national policies for learners’ data or guidelines that govern the ethical usage of data in research or education. We also conclude, that learning analytics research on pre-university level to high extent have been overlooked. In the same vein, learning analytics has not received enough focus form national and European national bodies. Such funding is necessary for taking steps towards data-driven development of education.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121510594","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}
Based on the currently developing trend of so called Massive Open Online Courses it is obvious that learning videos are more in use nowadays. This is some kind of comeback because due to the maxim “TV is easy, book is hard” [1][2] videos were used rarely for teaching. A further reason for this rare usage is that it is widely known that a key factor for human learning is a mechanism called selective attention [3][4]. This suggests that managing this attention is from high importance. Such a management could be achieved by providing different forms of interaction and communication in all directions. It has been shown that interaction and communication is crucial for the learning process [6]. Because of these remarks this research study introduces an algorithm which schedules interactions in learning videos and live broadcastings. The algorithm is implemented by a web application and it is based on the concept of a state machine. Finally, the evaluation of the algorithm points out that it is generally working after the improvement of some drawbacks regarding the distribution of interactions in the video.
{"title":"Scheduling Interactions in Learning Videos: A State Machine Based Algorithm","authors":"Josef Wachtler, Martin Ebner","doi":"10.3991/IJAI.V1I1.10995","DOIUrl":"https://doi.org/10.3991/IJAI.V1I1.10995","url":null,"abstract":"Based on the currently developing trend of so called Massive Open Online Courses it is obvious that learning videos are more in use nowadays. This is some kind of comeback because due to the maxim “TV is easy, book is hard” [1][2] videos were used rarely for teaching. A further reason for this rare usage is that it is widely known that a key factor for human learning is a mechanism called selective attention [3][4]. This suggests that managing this attention is from high importance. Such a management could be achieved by providing different forms of interaction and communication in all directions. It has been shown that interaction and communication is crucial for the learning process [6]. Because of these remarks this research study introduces an algorithm which schedules interactions in learning videos and live broadcastings. The algorithm is implemented by a web application and it is based on the concept of a state machine. Finally, the evaluation of the algorithm points out that it is generally working after the improvement of some drawbacks regarding the distribution of interactions in the video.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123502912","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 happens not only in the traditional educational institutions but beyond them, and it is not limited to an established academic time period. We study anywhere, anytime; many different and tools and services that facilitate learning are not confined to a specific environment or platform. Such set of tools and services used for educational proposes set up what can be understood as Personal Learning Environments. While knowing what happens in Personal Learning Environments is highly interesting, it is not an easy task due to the heterogeneity of the different tools, the difficulty to access learning evidence, ethical aspects or interoperability problems, among others. This study explores the main issues that need to be addressed to analyze what is occurring in a Personal Learning Environments and how to represent the information obtained from such environments.
{"title":"Data Driven Education in Personal Learning Environments - What About Learning beyond the Institution?","authors":"Miguel Ángel Conde González, Á. Hernández-García","doi":"10.3991/ijai.v1i1.11041","DOIUrl":"https://doi.org/10.3991/ijai.v1i1.11041","url":null,"abstract":"Learning happens not only in the traditional educational institutions but beyond them, and it is not limited to an established academic time period. We study anywhere, anytime; many different and tools and services that facilitate learning are not confined to a specific environment or platform. Such set of tools and services used for educational proposes set up what can be understood as Personal Learning Environments. While knowing what happens in Personal Learning Environments is highly interesting, it is not an easy task due to the heterogeneity of the different tools, the difficulty to access learning evidence, ethical aspects or interoperability problems, among others. This study explores the main issues that need to be addressed to analyze what is occurring in a Personal Learning Environments and how to represent the information obtained from such environments.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123436783","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}
In this study, we employed a random control experiment to evaluate the effectiveness of gamification (e.g. scores, goal, progressive bar, etc.) and initial task difficulty on college student engagement in computer-based assessments. A group of Chinese college students (N=97) were randomly assigned to four groups obtained by crossing the two independent variables: gamification (with or without) and initial difficulty level (low or normal). The experiment lasted for 35 minutes, and each student was asked to complete several mini-tests (named maze tests). Student engagement was measured by the average off-task time between two maze tests. The results showed that both gamification and low-difficulty entry level reduced students’ off-task time. However, the gamification effect was only significant for male students but not for female students. The study also demonstrated that the maze test can be a potential method to predict the general English proficiency with Chinese English language learners.
{"title":"Gamification and Student Engagement with a Curriculum-based Measurement System","authors":"Yu Yan, Simon Hooper, Shi-Wei Pu","doi":"10.3991/IJAI.V1I1.10805","DOIUrl":"https://doi.org/10.3991/IJAI.V1I1.10805","url":null,"abstract":"In this study, we employed a random control experiment to evaluate the effectiveness of gamification (e.g. scores, goal, progressive bar, etc.) and initial task difficulty on college student engagement in computer-based assessments. A group of Chinese college students (N=97) were randomly assigned to four groups obtained by crossing the two independent variables: gamification (with or without) and initial difficulty level (low or normal). The experiment lasted for 35 minutes, and each student was asked to complete several mini-tests (named maze tests). Student engagement was measured by the average off-task time between two maze tests. The results showed that both gamification and low-difficulty entry level reduced students’ off-task time. However, the gamification effect was only significant for male students but not for female students. The study also demonstrated that the maze test can be a potential method to predict the general English proficiency with Chinese English language learners.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127887420","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}
In this paper, we suggest an ecological perspective on the role of analytics in education. We discuss how different stakeholder positions in education points to different interests in using analytics. As a point of reference, we examine the Danish case of ICT integration in primary and lower secondary school (Danish: Folkeskolen) in order to study cases of emerging and at times conflicting stakeholder interests. On this basis, we discuss how this complexity of the educational ecosystem affects different stakeholder positions within the field.
{"title":"The Ecology of Analytics in Education: Stakeholder Interests in Data-Rich Educational Systems","authors":"U. D. Berthelsen, M. Tannert","doi":"10.3991/IJAI.V1I1.11023","DOIUrl":"https://doi.org/10.3991/IJAI.V1I1.11023","url":null,"abstract":"In this paper, we suggest an ecological perspective on the role of analytics in education. We discuss how different stakeholder positions in education points to different interests in using analytics. As a point of reference, we examine the Danish case of ICT integration in primary and lower secondary school (Danish: Folkeskolen) in order to study cases of emerging and at times conflicting stakeholder interests. On this basis, we discuss how this complexity of the educational ecosystem affects different stakeholder positions within the field.","PeriodicalId":165037,"journal":{"name":"Int. J. Learn. Anal. Artif. Intell. Educ.","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127106556","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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