Learn Smarter, Not Harder – Exploring the Development of Learning Analytics Use Cases to Create Tailor-Made Online Learning Experiences

Our world is significantly shaped by digitalization, fostering new opportunities for technology-mediated learning. Therefore, massive amounts of knowledge become available online. However, concurrently these formats entail less interaction and guidance from lecturers. Thus, learners need to be supported by intelligent learning tools that provide suitable knowledge in a tailored way. In this context, the use of learning analytics in its multifaceted forms is essential. Existing literature shows a proliferation of learning analytics use cases without a systematic structure. Based on a structured literature review of 42 papers we organized existing literature contributions systematically and derived four use cases: learning dashboards, individualized content, tutoring systems, and adaptable learning process based on personality. Our use cases will serve as a basis for a targeted scientific discourse and are valuable orientation for the development of future learning analytics use cases to give rise to the new form of Learning Experience Platforms

AI-Powered Education: Exploring the Potential of Personalised Learning for Students' Needs in Indonesia Education

Artificial intelligence (AI) stands out as a relatively new yet rapidly expanding technological tool that is transforming the field of education. This paper examines the potential of Artificial Intelligence to assist students and teachers in personalised learning. The research methodology employed for this study is a literature review, providing an overview of the current knowledge on practical AI applications for personalised learning and insights into methodological developments in this research field. Based on the literature, the results of this research demonstrate that personalised learning effectively accommodates students' learning preferences and enhances academic performance. Therefore, it could significantly benefit students by accommodating their learning pace and style. In the Indonesian education system context, the integration of AI for personalised learning is already included in the Indonesia Artificial Intelligence National Plan framework. To support this plan, the authors employed a Personalized Learning Plan (PLP) to integrate AI into educational settings practically. However, a challenge in integrating AI into education for personalised learning is that course or class designers often pay insufficient attention to creating content that develops practical skills. This neglect of pedagogical and technical aspects has led students and teachers to perceive these systems as unresponsive to their learning preferences, fostering a sense of pessimism.

인공지능 기반 교육 플랫폼 사용에 대한 중국 교사의 인식

학위논문 (석사) -- 서울대학교 대학원 : 사범대학 교육학과, 2021. 2. 조영환.최근 교육 분야에서 인공지능(AI)의 도입이 큰 관심을 끌고 있다. 특히 AI 기술과 학습 분석이 결합한 인공지능 기반 교육 플랫폼은 지금껏 실현되기 어려웠던 맞춤형 학습(personalized learning)과 적응적 학습(adaptive learning)에 도움이 될 수 있도록 발전하고 있다. 인공지능 기반 교육 플랫폼(AI-based education platform)은 학습자의 행동 추적 등을 통해 이들의 특성을 분석하고 진단을 제공한 뒤 분석 결과를 토대로 학습자에게 인지 수준에 맞는 맞춤형 학습자원과 피드백을 제공한다. 인공지능 기반 교육 플랫폼은 교사와 학생에게 실시간 학습 데이터와 분석 결과, 그리고 피드백을 제공할 수 있어 인공지능 기반 교육 플랫폼이 맞춤형 학습에 긍정적인 의미가 있다는 선행 연구도 있었다. 그럼에도 불구하고, 기존 연구는 모델 개발의 차원에서나 엄밀한 실험실 환경에서 인공지능 기반 교육 플랫폼의 효과를 연구해왔으며, 인공지능 기반 교육 플랫폼에 대한 교사의 인식과 관련된 연구는 드물었다. 교사는 인공지능 교육 기술의 사용자이기 때문에 인공지능 교육 기술의 교육 도입에 있어 교사들의 인식과 의견은 중요하다. 본 연구는 인공지능 기반 교육 플랫폼을 활용하는 것에 대한 교사들의 인식을 탐구하였다. 아래 연구 문제를 다루기 위해 질적 연구를 시행하였다. 첫째, 중국 교사들은 인공지능 기반 교육 플랫폼이 중학교 교육에 활용 있어 어떠한 장점이 있다고 인식하는가? 둘째, 중국 교사들은 인공지능 기반 교육 플랫폼과 중학교 교수 활동 요소 간 어떠한 모순이 있다고 인식하는가? 셋째, 중국 교사들은 인공지능 기반 교육 플랫폼을 중학교 교육에 도입할 때 무엇이 필요하다고 인식하는가? 본 연구는 중국 교사들을 연구대상으로 온라인 심층 면담을 하였다. 문헌 리뷰를 통해 면담 질문지를 설계하되 눈덩이표집법 (snowball sampling)을 통해 중국 중학교 교사 14명을 연구참여자로 선정하였다. 선정된 교사들은 모두 인공지능 기반 교육 플랫폼 사용 경험이 있으며 각 교사를 대상으로 약 1시간 정도 면담을 진행하고 녹음하였다. 면담이 끝난 후 녹음 내용을 전사하였으며, 주제분석을 사용하여 면담 내용을 초기 코드 생성하고 면담 자료 속에서 주제를 도출하였다. 특히 연구 문제 2번의 경우, 인공지능 기반 교육 플랫폼 활용과 교수 학습활동 내 여러 요소 간의 모순을 분석하기 위해 활동이론을 연구의 틀로 이용하였다. 최종적으로 연구문제 1에 대한 주제 4개, 연구문제 2에 대한 주제 6개, 연구문제 3에 대한 주제 4개를 도출하였다. 연구 결과로 교사들은 인공지능 기반 교육 플랫폼의 장점에 대해 즉각적인 피드백 제공, 교수학습 지원, 교사의 업무량 감소 등으로 인식하였고, 인공지능 기반 교육 플랫폼이 다양한 교수학습 자원을 통합할 수 있다고 인식하였다. 아울러 교사들은 인공지능 기반 교육 플랫폼의 사용에 있어 기존의 교수학습 활동과 상충된 부분이 있다는 점을 인식하였다. 교사들은 기존 인공지능 기반 교육 플랫폼의 추천 모델이 차별화된 학생들에게 잘 적용되지 못한다는 것을 인식하였다. 그리고 기존 인공지능 기반 교육 플랫폼이 다양한 학습 자원을 잘 분류되지 못하기 때문에 교사들이 사용하기 불편하다. 인공지능 기반 교육 플랫폼을 이용할 때 교사의 지적재산권을 보호하기 위한 명확한 규제가 부족하다고 인식하였다. 이와 함께 학부모들은 인공지능 기반 교육 플랫폼을 사용함으로써 발생할 수 있는 학습자의 인터넷 남용과 시력 저하 문제를 우려하였다. 또 중국의 사회문화적 배경과 교육 특성으로 인해 인공지능 기반 교육 플랫폼을 활용하는 데 학생들의 글씨 쓰기 능력에 영향을 미칠 수 있으며, 학교 내 전자기기 사용 제한도 데이터 수집의 지속성과 효율성에 영향을 미칠 수 있다고 인식하였다. 교사들은 위의 문제들이 인공지능 교육 플랫폼 사용에 대한 규칙 마련과 인공지능 기술을 개선함으로써 완화될 수 있다고 인식하였다. 또한 교사의 실제 요구에 맞게 개발될 수 있도록 인공지능 기반 교육 플랫폼 개발 과정에 교육 전문가와 교사가 참여할 필요가 있다. 본 연구는 중국 교사들이 인공지능 기반 교육 플랫폼에 대한 인식을 탐색하였으며, 인공지능 기반 교육 플랫폼이 교수학습에서의 장점과 문제점을 밝혔다. 아울러 본 연구는 인공지능 기반 교육 플랫폼이 교육 분야에 대규모로 도입될 수 있도록 규칙, 인공지능 기술, 그리고 교육 공학의 차원에서 사용 규범과 기술 개선을 제안하였다. 본 연구를 통해 탐색한 내용이 향후 교육 분야의 인공지능 기반 교육 플랫폼 도입에 활용된다면 인공지능 교육 기술에 관한 연구의 발전에도 기여할 수 있을 것으로 기대된다.In recent years, the introduction of artificial intelligence (AI) in education has attracted widespread attention. In particular, the AI-based education platform based on the combination of AI technology and learning analysis brings new light to the long-standing difficulties in personalized learning and adaptive learning. The AI-based education platform analyzes learners' characteristics by collecting their data and tracking their learning behavior. It then generates cognitive diagnosis for learners and provides them with personalized learning resources and adaptive feedback that match their cognitive level based on systematic analysis. With the help of the AI-based education platform, teachers and students can get real-time educational data and analysis result，as well as the feedback and treatment corresponding to the results. Previous studies have already demonstrated and proved its positive significance to personalized learning. However, these studies mostly start from a model development perspective or in a rigorous laboratory environment. There has been little research on teachers' perceptions of AI-based education platform. As a direct user of AI educational technologies, teachers' perceptions and suggestions are vital for introducing AIEd in education. In this study, the researcher explored teachers' perceptions of using AI-based education platform in teaching. The study conducted qualitative research to address the following research questions: 1) How do Chinese teachers perceive the advantages of AI-based education platforms for teaching and learning in secondary school? 2) How do Chinese teachers perceive the contradictions between AI-based education platforms and the secondary school system? 3）How do Chinese teachers suggest applying AI-based education platforms in secondary school? And it referred to the in-depth online interview with Chinese teachers who had experience with AI-based education platform. Interview questions were constructed through the literature review, and 14 secondary school teachers were selected by the snowball sampling method. The interviews lasted for an average of one hour per teacher and were transcribed from the audio recordings to text documents when finished. Afterward, the data were analyzed using thematic analysis, including generating initial codes, searching and reviewing the categories, and deriving the themes finally. Notably, for research question two, the researcher used the activity theory framework to analyze the contradictions among the use of the AI-based education platform and the various elements of the teaching and learning activities. Finally, four themes for research question 1, six themes for research question 2, and four themes for research question 3 were derived. As for the advantages, teachers believe that AI-based education platforms can provide instant feedback, targeted and systematic teaching support, and reduce teachers' workload. At the same time, AI-based education platforms can also integrate teaching resources in different areas. Teachers also recognized that the AI-based education platforms might trigger contradictions in existing teaching activities. They are aware of the situation that the recommended model of the AI-based education platform is not suitable for all levels of students; that a large number of learning resources are not classified properly enough to meet the needs of teachers, and that there lack clear rules and regulations to protect teachers' intellectual property rights when using the platform. Besides, parents are also concerned about the potential risk of internet addiction and vision problems using AI-based education platforms. Moreover, the use of the AI-based education platform may also affect students' ability to write Chinese characters due to the socio-historical background and educational characteristics in China. Furthermore, the restricted use of electronic devices on campus may also impact the consistent and effective education data collection. Teachers believe that these problems can be solved by improving rules and AI technology. Moreover, to make the platform more in line with the actual teaching requirements, teachers and education experts can also be involved in the development process of AI-based education platform. This study explored how Chinese teachers perceive the AI-based education platform and found that the AI-based education platform was conducive to personalized teaching and learning. At the same time, this study put forward some suggestions from the perspective of rules, AI technology, and educational technology, hoping to provide a good value for the future large-scale introduction of AI-based education platforms in education.CHAPTER 1. INTRODUCTION 1 1.1. Problem Statement 1 1.2. Purpose of Research 7 1.3. Definition of Terms 8 CHAPTER 2. LITERATURE REVIEW 10 2.1. AI in Education 10 2.1.1 AI for Learning and Teaching 10 2.1.2 AI-based Education Platform 14 2.1.3 Teachers' Perception on AI-based Education Platform 18 2.2. Activity Theory 20 CHAPTER 3. RESEARCH METHOD 23 3.1. Research Design 23 3.2. Participants 25 3.3. Instrumentation 26 3.3.1 Potential Value of AI System in Education 26 3.4. Data Collection 33 3.5. Data Analysis 34 CHAPTER 4. FINDINGS 36 4.1. Advantages of Using AI-based Education Platform 36 4.1.1 Instant Feedback 37 4.1.2 Targeted and Systematic Teaching Support 42 4.1.3 Educational Resources Sharing 46 4.1.4 Reducing Workload 49 4.2. Tensions of Using AI-based Education Platform 51 4.2.1 Inadequately Meet the Needs of Teachers 52 4.2.2 Failure to Satisfy Low and High Achievers 54 4.2.3 Intellectual Property Violation 56 4.2.4 Guardian's Concern 57 4.2.5 School Rules about the Use of Electronic Devices 58 4.2.6 Implication for Chinese Character Education 59 4.3. Suggestion of Using AI-based Education Platform 61 4.3.1 Improving Rules of Using the AI-based Education Platform 61 4.3.2 Improving Rules of Protecting Teachers Right 62 4.3.3 Improving AI Technology 64 4.3.4 Participatory Design 66 CHAPTER 5. DISCUSSION AND CONCLUSION 68 5.1. Discussion 68 5.2. Conclusion 72 REFERENCE 75 APPENDIX 1 98 APPENDIX 2 100 국문초록 112Maste

Five Lenses on Team Tutor Challenges: A Multidisciplinary Approach

This chapter describes five disciplinary domains of research or lenses that contribute to the design of a team tutor. We focus on four significant challenges in developing Intelligent Team Tutoring Systems (ITTSs), and explore how the five lenses can offer guidance for these challenges. The four challenges arise in the design of team member interactions, performance metrics and skill development, feedback, and tutor authoring. The five lenses or research domains that we apply to these four challenges are Tutor Engineering, Learning Sciences, Science of Teams, Data Analyst, and Human–Computer Interaction. This matrix of applications from each perspective offers a framework to guide designers in creating ITTSs

Modeling Tutoring Knowledge

This is a preliminary version of the chapter, the final one can be accessed at http://www.springerlink.com/content/978-3-642-14362-5#section=784256&page=1&locus=29This chapter introduces the topic "modeling tutoring knowledge" in ITS research. Starting with its origin and with a characterization of tutoring, it proposes a general definition of tutoring, and a description of tutoring functions, variables, and interactions. The Interaction Hypothesis is presented and discussed, followed by the development of the tutorial component of ITSs, and their evaluation. New challenges are described, such as integrating the emotional states of the learner. Perspectives of opening the Tutoring Model and of equipping it with social intelligence are also presented

Knowledge mentoring as a framework for designing computer-based agents for supporting musical composition learning

An approach to the design of teaching agents in problem-seeking domains - that is based on a systematic relationship between theoretical framework, analysis of empirical data, computational model and computational implementation - has been developed. The theoretical framework, called the Knowledge Mentoring framework (KMf), was developed to investigate how studies of dialogue and interaction can be exploited in a practical way by designers of computer-based teaching agents. A particular focus was the following musical education problem: when interacting with a computer-based music system, many students do not spontaneously reflect on their activity, they often need to be encouraged to do this. The KMf provides a taxonomy and definitions of the pedagogical goals involved in a 'mentoring' style of teaching. Mentoring is an approach to teaching that aims to support learners' creative, metacognitive and critical thinking, these being essential to musical composition and other open-ended, problem-seeking domains. This theoretical framework was used to guide the analysis and modelling of data produced by an empirical study of human teacher-learner interactions. Information on the temporal ordering of teacher-learner interactions was revealed (modelled as. state transition networks and a mentoring script). Findings from the analysis also included a pause taxonomy (that provided evidence of a link between pause length and learner ability) and the occurrence of reciprocal modelling (where participants in learning interactions built up models of the other participants' expectations). The theoretical framework and the analysis findings were then used to develop a computational model for teaching agents in problem-seeking domains. Aspects of our theory, analysis findings and computational model were incorporated into a computational implementation: a pre-prototype teaching agent called MetaMuse. A Cooperative Evaluation of MetaMuse with teacher-composers showed that it had the potential to promote creative reflection in learners

Developing Student Model for Intelligent Tutoring System

The effectiveness of an e-learning environment mainly encompasses on how efficiently the tutor presents the learning content to the candidate based on their learning capability. It is therefore inevitable for the teaching community to understand the learning style of their students and to cater for the needs of their students. One such system that can cater to the needs of the students is the Intelligent Tutoring System (ITS). To overcome the challenges faced by the teachers and to cater to the needs of their students, e-learning experts in recent times have focused in Intelligent Tutoring System (ITS). There is sufficient literature that suggested that meaningful, constructive and adaptive feedback is the essential feature of ITSs, and it is such feedback that helps students achieve strong learning gains. At the same time, in an ITS, it is the student model that plays a main role in planning the training path, supplying feedback information to the pedagogical module of the system. Added to it, the student model is the preliminary component, which stores the information to the specific individual learner. In this study, Multiple-choice questions (MCQs) was administered to capture the student ability with respect to three levels of difficulty, namely, low, medium and high in Physics domain to train the neural network. Further, neural network and psychometric analysis were used for understanding the student characteristic and determining the student’s classification with respect to their ability. Thus, this study focused on developing a student model by using the Multiple-Choice Questions (MCQ) for integrating it with an ITS by applying the neural network and psychometric analysis. The findings of this research showed that even though the linear regression between real test scores and that of the Final exam scores were marginally weak (37%), still the success of the student classification to the extent of 80 percent (79.8%) makes this student model a good fit for clustering students in groups according to their common characteristics. This finding is in line with that of the findings discussed in the literature review of this study. Further, the outcome of this research is most likely to generate a new dimension for cluster based student modelling approaches for an online learning environment that uses aptitude tests (MCQ’s) for learners using ITS. The use of psychometric analysis and neural network for student classification makes this study unique towards the development of a new student model for ITS in supporting online learning. Therefore, the student model developed in this study seems to be a good model fit for all those who wish to infuse aptitude test based student modelling approach in an ITS system for an online learning environment. (Abstract by Author

Enabling Context Aware Applications in Learning Environments

Master'sMASTER OF SCIENC

Detecting and Modelling Stress Levels in E-Learning Environment Users

A modern Intelligent Tutoring System (ITS) should be sentient of a learner's cognitive and affective states, as a learner’s performance could be affected by motivational and emotional factors. It is important to design a method that supports low-cost, task-independent and unobtrusive sensing of a learner’s cognitive and affective states, to improve a learner's experience in e-learning, as well as to enable personalized learning. Although tremendous related affective computing research were done in this area, there is a lack of empirical research that can automatically measure a learner's stress using objective methods. This research is set to examine how an objective stress measurement model can be developed, to compute a learner’s cognitive and emotional stress automatically using mouse and keystroke dynamics. To ensure the measurement is not affected even if the user switches between tasks, three preliminary research experiments were carried out based on three common tasks during e-learning − search, assessment and typing. A stress measurement model was then built using the datasets collected from the experiments. Three stress classifiers were tested, namely certainty factors, feedforward back-propagation neural network and adaptive neuro-fuzzy inference system. The best classifier was then integrated into the ITS stress inference engine, which is designed to decide necessary adaptation, and to provide analytical information of learners' performances, which include stress levels and learners’ behaviours when answering questions