Project

International Journal of Learning Analytics and Artificial Intelligence for Education (www.i-jai.org)

Goal: The newly launched International Journal of Learning Analytics and Artificial Intelligence for Education (www.i-jai.org) aims to increase knowledge and understanding of ways in which learning analytics (LA) and artificial intelligence (AI) can support and enhance education. The journal aims to bridge the gap between pure academic research journals and more practical publications.

The open journal will be indexed in databases such as Scopus, DOAJ, DLBP, LearnTechLib, EBSCO, Google Scholar, etc.

This journal provides open access to all of its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. Such access is associated with increased readership and increased citation of an author's work.

We invite researchers and relevant stakeholder to submit papers that for instance focus on:

* Analysing Learner’s Activity, Engagement, and Motivation
* Modeling Learning and Teaching in Different Environments (online, blended and physical environments)
* Evaluation and Development of Teaching Practices and Learning Designs
* Application of Machine Learning, Educational Data Mining, and Predictive Analytics
* Social Network Analysis and Network Modelling Tools
* Supporting and Developing Assessment and Feedback Practices
* Supporting Personalised and Adaptive Learning
* Analyzing and Improvement of Learning Organisations based on Data
* Development of new Methods and Theories
* AI applications in the educational domain

See more at www.i-jai.org

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Jalal Nouri
added an update
Call for paper of a Special issue on Positioning Artificial Intelligence in Education - Evidence and Reflections
International Journal of Learning Analytics and Artificial Intelligence for Education
Special Issue Editors Jalal Nouri, Stockholm University Dirk Ifenthaler, Curtin University Martin Ebner, Graz University of Technology Mohammed Saqr, University of Eastern Finland
Introduction Due to recent technological advancements, artificial intelligence (AI) has received increased attention and has been adopted to many sectors and fields, thus, producing a profound impact. Given the rapid advancement of AI, it is expected that AI will continue to develop, integrate deeper and in many different sectors. Although the application of AI in the educational sector has been the subject of research for more than 30 years, a renewed and enforced interest in AI in education can be documented through the themes of scientific conferences, workshops, and research papers, as well as how the Edtech industry is increasing efforts to integrate AI in educational applications. Indeed, this development is not unexpected considering that AI is associated with the potentials of more personalized, adaptive and inclusive learning, and with empowered teachers and advanced learning environments. This Special Issue aims at highlighting contemporary research that covers AI in education. We are looking forward to receiving both theoretical and empirical papers that provide readers with a better understanding of the theoretical discussions that are currently taking place, the empirical studies that are conducted, and the AI applications and systems that are developed.
Topics of interest include, but are not limited to: · Adaptive and intelligent learning systems · Natural language processing applications in education · Assessment and testing of learning outcomes using AI · Intelligent training systems · Student modeling and cognitive diagnosis using AI · Deep learning application in education · Ethical perspectives on AI in education
Submission Submit the article at www.i-jai.org
Instructions for authors Manuscripts must be written in English using the following template: http://icl-conference.org/ojs_logos/iJXX_authorguide.docx. Submitted contributions undergo a double-blind review and will be published open access. In order to make this possible, please submit an anonymized file without the authors' names and a non-blinded version with author names and affiliations. More information for authors is available at: https://www.online-journals.org/index.php/i-jai/about/submissions#authorGuidelines
Important dates Submission of full paper: January 20, 2020
Contact Jalal Nouri, jalal@i-jai.com Dirf Ifenthaler, dirk@ifenthaler.info Martin Ebner, martin.ebner@tugraz.at Mohammed Saqr, mohammed.saqr@uef.fi
 
Jalal Nouri
added an update
Call for paper of a Special issue on Positioning Artificial Intelligence in Education - Evidence and Reflections
International Journal of Learning Analytics and Artificial Intelligence for Education
Special Issue Editors
Jalal Nouri, Stockholm University
Dirk Ifenthaler, Curtin University
Martin Ebner, Graz University of Technology
Mohammed Saqr, University of Eastern Finland
Introduction
Due to recent technological advancements, artificial intelligence (AI) has received increased attention and has been adopted to many sectors and fields, thus, producing a profound impact. Given the rapid advancement of AI, it is expected that AI will continue to develop, integrate deeper and in many different sectors. Although the application of AI in the educational sector has been the subject of research for more than 30 years, a renewed and enforced interest in AI in education can be documented through the themes of scientific conferences, workshops and research papers, as well as how the Edtech industry is increasing efforts to integrate AI in educational applications. Indeed, this development is not unexpected considering that AI is associated with potentials of more personalized, adaptive and inclusive learning, and with empowered teachers and advanced learning environments.
This Special Issue aims at highlighting contemporary research that covers AI in education. We are looking forward to receive both theoretical and empirical papers that provide readers with a better understanding of the theoretical discussions that are currently taking place, the empirical studies that are conducted, and the AI applications and systems that are developed.
Topics of interest include, but are not limited to:
· Adaptive and intelligent learning systems
· Natural language processing applications in education
· Assessment and testing of learning outcomes using AI
· Intelligent training systems
· Student modelling and cognitive diagnosis using AI
· Deep learning application in education
· Ethical perspectives on AI in education
Submission
Send your work per mail to: jalal@i-jai.com
Instructions for authors
Manuscripts must be written in English using the following template: http://icl-conference.org/ojs_logos/iJXX_authorguide.docx. Submitted contributions undergo a double-blind review and will be published open access. In order to make this possible, please submit an anonymized file without the authors' names and a non-blinded version with author names and affiliations. More information for authors is available at: https://www.online-journals.org/index.php/i-jai/about/submissions#authorGuidelines
Important dates
Submission of full paper: December 10, 2019
Contact
Jalal Nouri, jalal@i-jai.com
Dirf Ifenthaler, dirk@ifenthaler.info
Mohammed Saqr, mohammed.saqr@uef.fi
 
Jalal Nouri
added 3 research items
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 aims to disseminate the 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.
The bachelor thesis is commonly a necessary last step towards the first graduation in higher education and constitutes a central key to both further studies in higher education and employment that requires higher education degrees. Thus, completion of the thesis is a desirable outcome for individual students, academic institutions and society, and non-completion is a significant cost. Unfortunately, many academic institutions around the world experience that many thesis projects are not completed and that students struggle with the thesis process. This paper addresses this issue with the aim to, on the one hand, identify and explain why thesis projects are completed or not, and on the other hand, to predict non-completion and completion of thesis projects using machine learning algorithms. The sample for this study consisted of bachelor students’ thesis projects (n=2436) that have been started between 2010 and 2017. Data were extracted from two different data systems used to record data about thesis projects. From these systems, thesis project data were collected including variables related to both students and supervisors. Traditional statistical analysis (correlation tests, t-tests and factor analysis) was conducted in order to identify factors that influence non-completion and completion of thesis projects and several machine learning algorithms were applied in order to create a model that predicts completion and non-completion. When taking all the analysis mentioned above into account, it can be concluded with confidence that supervisors’ ability and experience play a significant role in determining the success of thesis projects, which, on the one hand, corroborates previous research. On the other hand, this study extends previous research by pointing out additional specific factors, such as the time supervisors take to complete thesis projects and the ratio of previously unfinished thesis projects. It can also be concluded that the academic title of the supervisor, which was one of the variables studied, did not constitute a factor for completing thesis projects. One of the more novel contributions of this study stems from the application of machine learning algorithms that were used in order to – reasonably accurately – predict thesis completion/non-completion. Such predictive models offer the opportunity to support a more optimal matching of students and supervisors.
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, 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 the pre-university level to a 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.
Jalal Nouri
added an update
We are happy to announce that the first issue of the International Journal of Learning Analytics and Artificial Intelligence for Education has been published and is available at https://online-journals.org/index.php/i-jai/issue/view/424
*Table of Contents* -------------------
***Editorial of the First Issue of the International Journal of Learning Analytics and Artificial Intelligence for Education (Jalal  Nouri)
*Papers*
***Efforts in Europe for Data-Driven Improvement of Education – A Review of Learning Analytics Research in Six Countries (Jalal  Nouri, Martin  Ebner, Dirk  Ifenthaler, Mohammed  Saqr, Jonna Malmberg, Mohammad  Khalil, Jesper  Bruun, Olga  Viberg, Miguel Ángel Conde González, Zacharoula  Papamitsiou, Ulf Dalvad  Berthelsen)
***Higher Education Stakeholders’ Views on Learning Analytics Policy Recommendations for Supporting Study Success (Dirk  Ifenthaler, Jane Yin-Kim  Yau)
***Data Driven Education in Personal Learning Environments – What About Learning beyond the Institution? (Miguel Á.  Conde, Ángel  Hernández-García)
***Scheduling Interactions in Learning Videos: A State Machine Based Algorithm (Josef  Wachtler, Martin  Ebner)
***Potentials of Chatbots for Spell Check among Youngsters (Jeton  Arifi, Markus  Ebner, Martin  Ebner)
***The Ecology of Analytics in Education: Stakeholder Interests in Data-Rich Educational Systems (Ulf Dalvad Berthelsen, Morten  Tannert)
***Gamification and Student Engagement with a Curriculum-based Measurement System (Yu  Yan, Simon  Hooper, Shi  Pu)
***Bachelor Thesis Analytics: Using Machine Learning to Predict Dropout and Identify Performance Factors (Jalal  Nouri, Ken  Larsson, Mohammed  Saqr)
 
Jalal Nouri
added a project goal
The newly launched International Journal of Learning Analytics and Artificial Intelligence for Education (www.i-jai.org) aims to increase knowledge and understanding of ways in which learning analytics (LA) and artificial intelligence (AI) can support and enhance education. The journal aims to bridge the gap between pure academic research journals and more practical publications.
The open journal will be indexed in databases such as Scopus, DOAJ, DLBP, LearnTechLib, EBSCO, Google Scholar, etc.
This journal provides open access to all of its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. Such access is associated with increased readership and increased citation of an author's work.
We invite researchers and relevant stakeholder to submit papers that for instance focus on:
* Analysing Learner’s Activity, Engagement, and Motivation
* Modeling Learning and Teaching in Different Environments (online, blended and physical environments)
* Evaluation and Development of Teaching Practices and Learning Designs
* Application of Machine Learning, Educational Data Mining, and Predictive Analytics
* Social Network Analysis and Network Modelling Tools
* Supporting and Developing Assessment and Feedback Practices
* Supporting Personalised and Adaptive Learning
* Analyzing and Improvement of Learning Organisations based on Data
* Development of new Methods and Theories
* AI applications in the educational domain
See more at www.i-jai.org