A Review of Data Mining in Personalized Education: Current Trends and Future Prospects

Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven by data mining techniques, it not only benefits students but also provides educators and institutions with tools to craft customized learning experiences. To offer a comprehensive review of recent advancements in personalized educational data mining, this paper focuses on four primary scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis. This paper presents a structured taxonomy for each area, compiles commonly used datasets, and identifies future research directions, emphasizing the role of data mining in enhancing personalized education and paving the way for future exploration and innovation.Comment: 25 pages, 5 figure

Sustainability Conversations for Impact: Transdisciplinarity on Four Scales

Sustainability is a dynamic, multi-scale endeavor. Coherence can be lost between scales – from project teams, to organizations, to networks, and, most importantly, down to conversations. Sustainability researchers have embraced transdisciplinarity, as it is grounded in science, shared language, broad participation, and respect for difference. Yet, transdisciplinarity at these four scales is not well-defined. In this dissertation I extend transdisciplinarity out from the project to networks and organizations, and down into conversation, adding novel lenses and quantitative approaches. In Chapter 2, I propose transdisciplinarity incorporate academic disciplines which help cross scales: Organizational Learning, Knowledge Management, Applied Cooperation, and Data Science. In Chapter 3 I then use a mixed-method approach to study a transdisciplinary organization, the Maine Aquaculture Hub, as it develops strategy. Using social network analysis and conversation analytics, I evaluate how the Hub’s network-convening, strategic thinking and conversation practices turn organization-scale transdisciplinarity into strategic advantage. In Chapters 4 and 5, conversation is the nexus of transdisciplinarity. I study seven public aquaculture lease scoping meetings (informal town halls) and classify conversation activity by “discussion discipline,” i.e., rhetorical and social intent. I compute the relationship between discussion discipline proportions and three sustainability outcomes of intent-to-act, options-generation, and relationship-building. I consider exogenous factors, such as signaling, gender balance, timing and location. I show that where inquiry is high, so is innovation. Where acknowledgement is high, so is intent-to-act. Where respect is high, so is relationship-building. Indirectness and sarcasm dampen outcomes. I propose seven interventions to improve sustainability conversation capacity, such as nudging, networks, and using empirical models. Chapter 5 explores those empirical models: I use natural language-processing (NLP) to detect the discussion disciplines by training a model using the previously coded transcripts. Then I use that model to classify 591 open-source conversation transcripts, and regress the sustainability outcomes, per-transcript, on discussion discipline proportions. I show that all three conversation outcomes can be predicted by the discussion disciplines, and most statistically-significant being intent-to-act, which responds directly to acknowledgement and respect. Conversation AI is the next frontier of transdisciplinarity for sustainability solutions

Knowledge Graph Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness

Presently, knowledge graph-based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships and do not effectively model exercise-rich features, such as exercise representativeness and informativeness. Consequently, this paper proposes a framework, namely the Knowledge-Graph-Exercise Representativeness and Informativeness Framework, to address these two issues. The framework consists of four intricate components and a novel cognitive diagnosis model called the Neural Attentive cognitive diagnosis model. These components encompass the informativeness component, exercise representation component, knowledge importance component, and exercise representativeness component. The informativeness component evaluates the informational value of each question and identifies the candidate question set that exhibits the highest exercise informativeness. Furthermore, the skill embeddings are employed as input for the knowledge importance component. This component transforms a one-dimensional knowledge graph into a multi-dimensional one through four class relations and calculates skill importance weights based on novelty and popularity. Subsequently, the exercise representativeness component incorporates exercise weight knowledge coverage to select questions from the candidate question set for the tested question set. Lastly, the cognitive diagnosis model leverages exercise representation and skill importance weights to predict student performance on the test set and estimate their knowledge state. To evaluate the effectiveness of our selection strategy, extensive experiments were conducted on two publicly available educational datasets. The experimental results demonstrate that our framework can recommend appropriate exercises to students, leading to improved student performance.Comment: 31 pages, 6 figure

Deep Reinforcement Learning Approaches for Technology Enhanced Learning

Artificial Intelligence (AI) has advanced significantly in recent years, transforming various industries and domains. Its ability to extract patterns and insights from large volumes of data has revolutionised areas such as image recognition, natural language processing, and autonomous systems. As AI systems become increasingly integrated into daily human life, there is a growing need for meaningful collaboration and mutual engagement between humans and AI, known as Human-AI Collaboration. This collaboration involves combining AI with human workflows to achieve shared objectives. In the current educational landscape, the integration of AI methods in Technology Enhanced Learning (TEL) has become crucial for providing high-quality education and facilitating lifelong learning. Human-AI Collaboration also plays a vital role in the field of Technology Enhanced Learning (TEL), particularly in Intelligent Tutoring Systems (ITS). The COVID-19 pandemic has further emphasised the need for effective educational technologies to support remote learning and bridge the gap between traditional classrooms and online platforms. To maximise the performance of ITS while minimising the input and interaction required from students, it is essential to design collaborative systems that effectively leverage the capabilities of AI and foster effective collaboration between students and ITS. However, there are several challenges that need to be addressed in this context. One challenge is the lack of clear guidance on designing and building user-friendly systems that facilitate collaboration between humans and AI. This challenge is relevant not only to education researchers but also to Human-Computer Interaction (HCI) researchers and developers. Another challenge is the scarcity of interaction data in the early stages of ITS development, which hampers the accurate modelling of students' knowledge states and learning trajectories, known as the cold start problem. Moreover, the effectiveness of Intelligent Tutoring Systems (ITS) in delivering personalised instruction is hindered by the limitations of existing Knowledge Tracing (KT) models, which often struggle to provide accurate predictions. Therefore, addressing these challenges is crucial for enhancing the collaborative process between humans and AI in the development of ITS. This thesis aims to address these challenges and improve the collaborative process between students and ITS in TEL. It proposes innovative approaches to generate simulated student behavioural data and enhance the performance of KT models. The thesis starts with a comprehensive survey of human-AI collaborative systems, identifying key challenges and opportunities. It then presents a structured framework for the student-ITS collaborative process, providing insights into designing user-friendly and efficient systems. To overcome the challenge of data scarcity in ITS development, the thesis proposes two student modelling approaches: Sim-GAIL and SimStu. SimStu leverages a deep learning method, the Decision Transformer, to simulate student interactions and enhance ITS training. Sim-GAIL utilises a reinforcement learning method, Generative Adversarial Imitation Learning (GAIL), to generate high-fidelity and diverse simulated student behavioural data, addressing the cold start problem in ITS training. Furthermore, the thesis focuses on improving the performance of KT models. It introduces the MLFBKT model, which integrates multiple features and mines latent relations in student interaction data, aiming to improve the accuracy and efficiency of KT models. Additionally, the thesis proposes the LBKT model, which combines the strengths of the BERT model and LSTM to process long sequence data in KT models effectively. Overall, this thesis contributes to the field of Human-AI collaboration in TEL by addressing key challenges and proposing innovative approaches to enhance ITS training and KT model performance. The findings have the potential to improve the learning experiences and outcomes of students in educational settings

Identifying optimal course structures using topic models

This research project investigates whether there exists an optimal way to structure topics in educational course content that results in higher levels of engagement among students. It is implemented by fitting topic models to transcripts of educational videos contained in the Khan Academy platform. The fitted models were used to extract topic trajectories across time for each video and subsequently clustered based on whether they have similar “shapes”. The differences in mean engagement metrics per cluster suggest that some course shapes are more palatable to students regardless of subject matter. Additionally, the topic trajectories suggest a constant progression of topics with little repetition is optimal for student engagement. The results from this project provide new methodologies to improve educational quality by focusing on the sequence of themes within instructional material

Machine Learning Models for Educational Platforms

Scaling up education online and onlife is presenting numerous key challenges, such as hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely. However, thanks to the wider availability of learning-related data and increasingly higher performance computing, Artificial Intelligence has the potential to turn such challenges into an unparalleled opportunity. One of its sub-fields, namely Machine Learning, is enabling machines to receive data and learn for themselves, without being programmed with rules. Bringing this intelligent support to education at large scale has a number of advantages, such as avoiding manual error-prone tasks and reducing the chance that learners do any misconduct. Planning, collecting, developing, and predicting become essential steps to make it concrete into real-world education. This thesis deals with the design, implementation, and evaluation of Machine Learning models in the context of online educational platforms deployed at large scale. Constructing and assessing the performance of intelligent models is a crucial step towards increasing reliability and convenience of such an educational medium. The contributions result in large data sets and high-performing models that capitalize on Natural Language Processing, Human Behavior Mining, and Machine Perception. The model decisions aim to support stakeholders over the instructional pipeline, specifically on content categorization, content recommendation, learners’ identity verification, and learners’ sentiment analysis. Past research in this field often relied on statistical processes hardly applicable at large scale. Through our studies, we explore opportunities and challenges introduced by Machine Learning for the above goals, a relevant and timely topic in literature. Supported by extensive experiments, our work reveals a clear opportunity in combining human and machine sensing for researchers interested in online education. Our findings illustrate the feasibility of designing and assessing Machine Learning models for categorization, recommendation, authentication, and sentiment prediction in this research area. Our results provide guidelines on model motivation, data collection, model design, and analysis techniques concerning the above applicative scenarios. Researchers can use our findings to improve data collection on educational platforms, to reduce bias in data and models, to increase model effectiveness, and to increase the reliability of their models, among others. We expect that this thesis can support the adoption of Machine Learning models in educational platforms even more, strengthening the role of data as a precious asset. The thesis outputs are publicly available at https://www.mirkomarras.com

Analytics of student interactions: towards theory-driven, actionable insights

The field of learning analytics arose as a response to the vast quantities of data that are increasingly generated about students, their engagement with learning resources, and their learning and future career outcomes. While the field began as a collage, adopting methods and theories from a variety of disciplines, it has now become a major area of research, and has had a substantial impact on practice, policy, and decision-making. Although the field supports the collection and analysis of a wide array of data, existing work has predominantly focused on the digital traces generated through interactions with technology, learning content, and other students. Yet for any analyses to support students and teachers, the measures derived from these data must (1) offer practical and actionable insight into learning processes and outcomes, and (2) be theoretically grounded. As the field has matured, a number of challenges related to these criteria have become apparent. For instance, concerns have been raised that the literature prioritises predictive modeling over ensuring that these models are capable of informing constructive actions. Furthermore, the methodological validity of much of this work has been challenged, as a swathe of recent research has found many of these models fail to replicate to novel contexts. The work presented in this thesis addresses both of these concerns. In doing so, our research is pervaded by three key concerns: firstly, ensuring that any measures developed are both structurally valid and generalise across contexts; secondly, providing actionable insight with regards to student engagement; and finally, providing representations of student interactions that are predictive of student outcomes, namely, grades and students’ persistence in their studies. This research programme is heavily indebted to the work of Vincent Tinto, who conceptually distinguishes between the interactions students have with the academic and social domains present within their educational institution. This model has been subjected to extensive empirical validation, using a range of methods and data. For instance, while some studies have relied upon survey responses, others have used social network metrics, demographic variables, and students’ time spent in class together to evaluate Tinto’s claims. This model provides a foundation for the thesis, and the work presented may be categorised into two distinct veins aligning with the academic and social aspects of integration that Tinto proposes. These two domains, Tinto argues, continually modify a student’s goals and commitments, resulting in persistence or eventual disengagement and dropout. In the former, academic domain, we present a series of novel methodologies developed for modeling student engagement with academic resources. In doing so, we assessed how an individual student’s behaviour may be modeled using hidden Markov models (HMMs) to provide representations that enable actionable insight. However, in the face of considerable individual differences and cross-course variation, the validity of such methods may be called into question. Accordingly, ensuring that any measurements of student engagement are both structurally valid, and generalise across course contexts and disciplines became a central concern. To address this, we developed our model of student engagement using sticky-HMMs, emphasised the more interpretable insight such an approach provides compared to competing models, demonstrated its cross-course generality, and assessed its structural validity through the successful prediction of student dropout. In the social domain, a critical concern was to ensure any analyses conducted were valid. Accordingly, we assessed how the diversity of social tie definitions may undermine the validity of subsequent modeling practices. We then modeled students’ social integration using graph embedding techniques, and found that not only are student embeddings predictive of their final grades, but also of their persistence in their educational institution. In keeping with Tinto’s model, our research has focused on academic and social interactions separately, but both avenues of investigation have led to the question of student disengagement and dropout, and how this may be represented and remedied through the provision of actionable insight

Automatically Detecting Confusion and Conflict During Collaborative Learning Using Linguistic, Prosodic, and Facial Cues

During collaborative learning, confusion and conflict emerge naturally. However, persistent confusion or conflict have the potential to generate frustration and significantly impede learners' performance. Early automatic detection of confusion and conflict would allow us to support early interventions which can in turn improve students' experience with and outcomes from collaborative learning. Despite the extensive studies modeling confusion during solo learning, there is a need for further work in collaborative learning. This paper presents a multimodal machine-learning framework that automatically detects confusion and conflict during collaborative learning. We used data from 38 elementary school learners who collaborated on a series of programming tasks in classrooms. We trained deep multimodal learning models to detect confusion and conflict using features that were automatically extracted from learners' collaborative dialogues, including (1) language-derived features including TF-IDF, lexical semantics, and sentiment, (2) audio-derived features including acoustic-prosodic features, and (3) video-derived features including eye gaze, head pose, and facial expressions. Our results show that multimodal models that combine semantics, pitch, and facial expressions detected confusion and conflict with the highest accuracy, outperforming all unimodal models. We also found that prosodic cues are more predictive of conflict, and facial cues are more predictive of confusion. This study contributes to the automated modeling of collaborative learning processes and the development of real-time adaptive support to enhance learners' collaborative learning experience in classroom contexts.Comment: 27 pages, 7 figures, 7 table