Continuous use of authoring for adaptive educational hypermedia : a long-term case study

Adaptive educational hypermedia allows lessons to be personalized according to the needs of the learner. However, to achieve this, content must be split into stand-alone fragments that can be processed by a course personalization engine. Authoring content for this process is still a difficult activity, and it is essential for the popularization of adaptive educational hypermedia that authoring is simplified, so that the various stakeholders in the educational process, students, teachers, administrators, etc. can easily work with such systems. Thus, real-world testing with these stakeholders is essential. In this paper we describe recent extensions and improvements we have implemented in the My Online Teacher MOT3.0 adaptation authoring tool set, based on an initial set of short-term evaluations, and then focus on describing a long-term usage and assessment of the system

Collaborative Learning with Artificial Intelligence Speakers (CLAIS): Pre-Service Elementary Science Teachers' Responses to the Prototype

This research aims to demonstrate that AI can function not only as a tool for learning, but also as an intelligent agent with which humans can engage in collaborative learning (CL) to change epistemic practices in science classrooms. We adopted a design and development research approach, following the Analysis, Design, Development, Implementation and Evaluation (ADDIE) model, to prototype a tangible instructional system called Collaborative Learning with AI Speakers (CLAIS). The CLAIS system is designed to have 3-4 human learners join an AI speaker to form a small group, where humans and AI are considered as peers participating in the Jigsaw learning process. The development was carried out using the NUGU AI speaker platform. The CLAIS system was successfully implemented in a Science Education course session with 15 pre-service elementary science teachers. The participants evaluated the CLAIS system through mixed methods surveys as teachers, learners, peers, and users. Quantitative data showed that the participants' Intelligent-Technological, Pedagogical, And Content Knowledge was significantly increased after the CLAIS session, the perception of the CLAIS learning experience was positive, the peer assessment on AI speakers and human peers was different, and the user experience was ambivalent. Qualitative data showed that the participants anticipated future changes in the epistemic process in science classrooms, while acknowledging technical issues such as speech recognition performance and response latency. This study highlights the potential of Human-AI Collaboration for knowledge co-construction in authentic classroom settings and exemplify how AI could shape the future landscape of epistemic practices in the classroom

Evaluation of social personalized adaptive E-Learning environments : end-user point of view

The use of adaptations, along with the social affordances of collaboration and networking, carries a great potential for improving e-learning experiences. However, the review of the previous work indicates current e-learning systems have only marginally explored the integration of social features and adaptation techniques. The overall aim of this research, therefore, is to address this gap by evaluating a system developed to foster social personalized adaptive e-learning experiences. We have developed our first prototype system, Topolor, based on the concepts of Adaptive Educational Hypermedia and Social E-Learning. We have also conducted an experimental case study for the evaluation of the prototype system from different perspectives. The results show a considerably high satisfaction of the end users. This paper reports the evaluation results from end user point of view, and generalizes our method to a component-based evaluation framework

Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (e.g., question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs-based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency, and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state-of-the-art models (e.g., GPT-3/4), embracing the initiative of open-sourcing models/systems, and adopting a human-centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models

Chatbot-supported Thesis Writing: An Autoethnographic Report

The release of the large language model based chatbot ChatGPT in November 2022 has brought considerable attention to the subject of artificial intelligence, not only in the public. From the perspective of higher education, ChatGPT challenges various learning and assessment formats as it significantly reduces the effectiveness of their learning and assessment functionalities. In particular, ChatGPT might be applied to formats that require learners to generate text, such as bachelor theses or student research papers. Accordingly, the research question arises to what extent writing of bachelor theses is still a valid learning and assessment format. Correspondingly, in this study, the first author was asked to write his bachelor's thesis exploiting ChatGPT. For tracing the impact of ChatGPT, methodically an autoethnographic approach was used. First, all considerations on the potential use of ChatGPT were documented in logs and secondly, all ChatGPT chats were logged. Both logs and chat histories were analyzed and are presented along to the recommendations for students regarding the use of ChatGPT suggested by Gimpel et al. (2023). In conclusion, ChatGPT is beneficial in thesis writing during various activities, such as brainstorming, structuring and text revision. However, there arise limitations, e.g., in referencing. Thus, ChatGPT requires a continuous validation of the outcomes generated fostering learning. Currently, ChatGPT is to be valued as a beneficial tool in thesis writing. However, writing a conclusive thesis still requires the learner's meaningful engagement. Accordingly, writing a thesis is still a valid learning and assessment format. With further releases of ChatGPT, an increase in capabilities is to be expected and the research question needs to be reevaluated from time to time.Comment: 26 page

Automatic assessment of text-based responses in post-secondary education: A systematic review

Text-based open-ended questions in academic formative and summative assessments help students become deep learners and prepare them to understand concepts for a subsequent conceptual assessment. However, grading text-based questions, especially in large courses, is tedious and time-consuming for instructors. Text processing models continue progressing with the rapid development of Artificial Intelligence (AI) tools and Natural Language Processing (NLP) algorithms. Especially after breakthroughs in Large Language Models (LLM), there is immense potential to automate rapid assessment and feedback of text-based responses in education. This systematic review adopts a scientific and reproducible literature search strategy based on the PRISMA process using explicit inclusion and exclusion criteria to study text-based automatic assessment systems in post-secondary education, screening 838 papers and synthesizing 93 studies. To understand how text-based automatic assessment systems have been developed and applied in education in recent years, three research questions are considered. All included studies are summarized and categorized according to a proposed comprehensive framework, including the input and output of the system, research motivation, and research outcomes, aiming to answer the research questions accordingly. Additionally, the typical studies of automated assessment systems, research methods, and application domains in these studies are investigated and summarized. This systematic review provides an overview of recent educational applications of text-based assessment systems for understanding the latest AI/NLP developments assisting in text-based assessments in higher education. Findings will particularly benefit researchers and educators incorporating LLMs such as ChatGPT into their educational activities.Comment: 27 pages, 4 figures, 6 table

Using High Performance Computing and Open Source Technologies for Solving Behaviour Analytics Problems in E-Learning

In this paper the authors describe solution for solving various analytical problems in E-learning, Course Management Systems like Moodle by using HPC (High Performance Computing) and Apache Hadoop open source technologies in Liepaja University. The problem is that nowadays there are collecting huge amounts of analytics data from several gigabytes to petabytes, which is hard to store, process, analyse and visualize. This article reflects one of the solutions concerning distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that can store and process the data, can scale without limits and provides technological opportunities of reliable, scalable and distributed computing.

Chapter 35 Digital Learning for Developing Asian Countries

Education – that is, the development of knowledge, skills, and values – is an important means by which to empower individuals in a society. As both a means towards and an outcome of gaining the capabilities necessary to participate in and contribute to society, education is an essential enabler in many social aspects, such as economic growth, poverty reduction, public health, and sustainable development, especially in today’s knowledge society. At the same time, however, education can still be a social institution that reflects and reproduces the social, cultural, and economic disadvantages that prevail in the rest of society (Bourdieu & Passeron, 1990). For example, students who are discriminated against socio-culturally or who are economically poor are more likely to receive an education that is characterized by inadequate infrastructure, few qualified teachers and encouraging peers, and outmoded pedagogical practices, which often results in a lower quality of life