EDUCATIONAL ANALYTICS OF AN OPENCOURSEWARE

Analytics as one of the recent fields in technology-based learning offers many benefits to educators, instructors, and administrators to improve the efficiency and quality of alternative educational materials, and learning experience through tracking and storing students’ log data on web platforms over an extended period of time. This mixed-method study investigates students’ log data retrieved from the opencourseware (OCW) specifically launched for a required academic English speaking skills course offered at Middle East Technical University in Turkey with the aim of enhancing the quality and efficiency of the materials available for the course. By understanding the reasons behind students’ behaviors via the interviews conducted with 50 students on this online courseware, this study also aims to provide useful practical hints to the instructors and guide them to act on future decisions. The analyzed data is based on learner behavior with a specific emphasis on average view duration, likes and dislikes, and comments. This study can serve as a starting point to guide and provide the instructors and administrators about the future of the aforementioned course which is also offered in a rotational hybrid learning format where the effectiveness of online materials gain even more importance

Using Learning Analytics to Devise Interactive Personalised Nudges for Active Video Watching

Videos can be a powerful medium for acquiring soft skills, where learning requires contextualisation in personal experience and ability to see different perspectives. However, to learn effectively while watching videos, students need to actively engage with video content. We implemented interactive notetaking during video watching in an active video watching system (AVW) as a means to encourage engagement. This paper proposes a systematic approach to utilise learning analytics for the introduction of adaptive intervention - a choice architecture for personalised nudges in the AVW to extend learning. A user study was conducted and used as an illustration. By characterising clusters derived from user profiles, we identify different styles of engagement, such as parochial learning, habitual video watching, and self-regulated learning (which is the target ideal behaviour). To find opportunities for interventions, interaction traces in the AVW were used to identify video intervals with high user interest and relevant behaviour patterns that indicate when nudges may be triggered. A prediction model was developed to identify comments that are likely to have high social value, and can be used as examples in nudges. A framework for interactive personalised nudges was then conceptualised for the case study

Open social student modeling in competency-based education

Ph.D

Inteligência artificial na educação e ética

Este capítulo aborda as questões éticas relacionadas com a aplicação da inteligência artificial (IA) na educação, desde os primórdios da inteligência artificial na educação, na década de 1970, até ao estado atual deste domínio, incluindo a crescente sofisticação das interfaces dos sistemas e o aumento da utilização e do abuso de dados. Enquanto nos primeiros tempos a maior parte das ferramentas estava virada para o aluno, atualmente existem ferramentas que estão viradas para o professor, apoiando a sua gestão da sala de aula, e para o administrador, ajudando-o na gestão de grupos de alunos. As ferramentas orientadas para o aluno têm agora em conta os aspetos afetivos e motivacionais da aprendizagem, bem como os aspetos cognitivos. O aumento da recolha de dados e das ferramentas de analítica da aprendizagem que lhe estão associadas tem permitido o desenvolvimento de dashboards para uma gestão dinâmica e a compreensão reflexiva dos alunos, dos professores e gestores. As questões éticas quase não tinham expressão nos primeiros tempos, mas atualmente são muito importantes. As razões devem-se aos receios legítimos de que a autonomia dos alunos e professores seja comprometida, de que os dados sejam recolhidos e desviados para outros fins, e de que a IA introduza nas decisões educacionais preconceitos adicionais aumentando a desigualdade já existente, e, também, devido à reputação assustadora que, em geral, a IA possui.info:eu-repo/semantics/publishedVersio

Ethics of AI in Education: Towards a Community-Wide Framework

While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically, to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community’s leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context

Ethics of AI in Education: Towards a Community-Wide Framework

While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically, to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community’s leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context

The TA Framework: Designing Real-time Teaching Augmentation for K-12 Classrooms

Recently, the HCI community has seen increased interest in the design of teaching augmentation (TA): tools that extend and complement teachers' pedagogical abilities during ongoing classroom activities. Examples of TA systems are emerging across multiple disciplines, taking various forms: e.g., ambient displays, wearables, or learning analytics dashboards. However, these diverse examples have not been analyzed together to derive more fundamental insights into the design of teaching augmentation. Addressing this opportunity, we broadly synthesize existing cases to propose the TA framework. Our framework specifies a rich design space in five dimensions, to support the design and analysis of teaching augmentation. We contextualize the framework using existing designs cases, to surface underlying design trade-offs: for example, balancing actionability of presented information with teachers' needs for professional autonomy, or balancing unobtrusiveness with informativeness in the design of TA systems. Applying the TA framework, we identify opportunities for future research and design.Comment: to be published in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 17 pages, 10 figure