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Zixuan Ke – ProQuest LLC, 2024
The essence of human intelligence lies in its ability to learn continuously, accumulating past knowledge to aid in future learning and problem-solving endeavors. In contrast, the current machine learning paradigm often operates in isolation, lacking the capacity for continual learning and adaptation. This deficiency becomes apparent in the face of…
Descriptors: Computational Linguistics, Computer Software, Barriers, Artificial Intelligence
Riordan Alfredo; Vanessa Echeverria; Linxuan Zhao; LuEttaMae Lawrence; Jie Xiang Fan; Lixiang Yan; Xinyu Li; Zachari Swiecki; Dragan Gaševic; Roberto Martinez-Maldonado – Journal of Learning Analytics, 2024
Despite growing interest in applying human-centred design methods to create learning analytics (LA) systems, most efforts have concentrated on initial design phases, with limited exploration of how LA tools and practices can coevolve during the actual learning and teaching activities. This paper examines how a human-centred LA dashboard can be…
Descriptors: Learning Analytics, Learning Management Systems, Artificial Intelligence, Computer Software
Yamauchi, Taisei; Flanagan, Brendan; Nakamoto, Ryosuke; Dai, Yiling; Takami, Kyosuke; Ogata, Hiroaki – Smart Learning Environments, 2023
In recent years, smart learning environments have become central to modern education and support students and instructors through tools based on prediction and recommendation models. These methods often use learning material metadata, such as the knowledge contained in an exercise which is usually labeled by domain experts and is costly and…
Descriptors: Mathematics Instruction, Classification, Algorithms, Barriers
Hanife Merve Erdogan; Nazan Sezen Yüksel – Acta Didactica Napocensia, 2023
The aim of this study is to classify the subjects and skills of middle school mathematics course in the context of MATH Taxonomy and to determine their relations. For this purpose, the questions and answers related to the mathematics subtest of a national exam were analyzed over the answers of 20154 students. The study continued with the analysis…
Descriptors: Mathematics Skills, Taxonomy, Computer Software, Probability
Ethan Prihar; Adam Sales; Neil Heffernan – Grantee Submission, 2023
This work proposes Dynamic Linear Epsilon-Greedy, a novel contextual multi-armed bandit algorithm that can adaptively assign personalized content to users while enabling unbiased statistical analysis. Traditional A/B testing and reinforcement learning approaches have trade-offs between empirical investigation and maximal impact on users. Our…
Descriptors: Trust (Psychology), Learning Management Systems, Learning Processes, Algorithms
Tanjea Ane; Tabatshum Nepa – Research on Education and Media, 2024
Precision education derives teaching and learning opportunities by customizing predictive rules in educational methods. Innovative educational research faces new challenges and affords state-of-the-art methods to trace knowledge between the teaching and learning ecosystem. Individual intelligence can only be captured through knowledge level…
Descriptors: Artificial Intelligence, Prediction, Models, Teaching Methods
Nesra Yannier; Scott E. Hudson; Henry Chang; Kenneth R. Koedinger – International Journal of Artificial Intelligence in Education, 2024
Adaptivity in advanced learning technologies offer the possibility to adapt to different student backgrounds, which is difficult to do in a traditional classroom setting. However, there are mixed results on the effectiveness of adaptivity based on different implementations and contexts. In this paper, we introduce AI adaptivity in the context of a…
Descriptors: Artificial Intelligence, Computer Software, Feedback (Response), Outcomes of Education
Chun Yan Enoch Sit; Siu-Cheung Kong – Journal of Educational Computing Research, 2024
Educational process mining aims (EPM) to help teachers understand the overall learning process of their students. Although deep learning models have shown promising results in many domains, the event log dataset in many online courses may not be large enough for deep learning models to approximate the probability distribution of students' learning…
Descriptors: Learning Processes, Learning Analytics, Algorithms, Guidelines
Karolína Dockalová Burská; Jakub Rudolf Mlynárik; Radek Ošlejšek – Education and Information Technologies, 2024
In cyber security education, hands-on training is a common type of exercise to help raise awareness and competence, and improve students' cybersecurity skills. To be able to measure the impact of the design of the particular courses, the designers need methods that can reveal hidden patterns in trainee behavior. However, the support of the…
Descriptors: Computer Science Education, Information Security, Computer Security, Training Methods
Yunus Kökver; Hüseyin Miraç Pektas; Harun Çelik – Education and Information Technologies, 2025
This study aims to determine the misconceptions of teacher candidates about the greenhouse effect concept by using Artificial Intelligence (AI) algorithm instead of human experts. The Knowledge Discovery from Data (KDD) process model was preferred in the study where the Analyse, Design, Develop, Implement, Evaluate (ADDIE) instructional design…
Descriptors: Artificial Intelligence, Misconceptions, Preservice Teachers, Natural Language Processing
Shou, Tianze; Borchers, Conrad; Karumbaiah, Shamya; Aleven, Vincent – International Educational Data Mining Society, 2023
Spatial analytics receive increased attention in educational data mining. A critical issue in stop detection (i.e., the automatic extraction of timestamped and located stops in the movement of individuals) is a lack of validation of stop accuracy to represent phenomena of interest. Next to a radius that an actor does not exceed for a certain…
Descriptors: Classroom Design, Accuracy, Validity, Space Utilization
Jamal Eddine Rafiq; Abdelali Zakrani; Mohammed Amraouy; Said Nouh; Abdellah Bennane – Turkish Online Journal of Distance Education, 2025
The emergence of online learning has sparked increased interest in predicting learners' academic performance to enhance teaching effectiveness and personalized learning. In this context, we propose a complex model APPMLT-CBT which aims to predict learners' performance in online learning settings. This systemic model integrates cognitive, social,…
Descriptors: Models, Online Courses, Educational Improvement, Learning Processes
Ni Li – International Journal of Web-Based Learning and Teaching Technologies, 2025
In depth exploration of how the pandemic has reshaped the education ecosystem over the past three years, especially in the context of the surge in demand for online education courses and learning platforms, this article focuses on the field of student ideological and political education, and innovatively constructs a moral and political education…
Descriptors: Artificial Intelligence, Computer Software, Technology Integration, Algorithms
Mingying Zheng – ProQuest LLC, 2024
The digital transformation in educational assessment has led to the proliferation of large-scale data, offering unprecedented opportunities to enhance language learning, and testing through machine learning (ML) techniques. Drawing on the extensive data generated by online English language assessments, this dissertation investigates the efficacy…
Descriptors: Artificial Intelligence, Computational Linguistics, Language Tests, English (Second Language)
Parhizkar, Amirmohammad; Tejeddin, Golnaz; Khatibi, Toktam – Education and Information Technologies, 2023
Increasing productivity in educational systems is of great importance. Researchers are keen to predict the academic performance of students; this is done to enhance the overall productivity of educational system by effectively identifying students whose performance is below average. This universal concern has been combined with data science…
Descriptors: Algorithms, Grade Point Average, Interdisciplinary Approach, Prediction
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