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Lokkila, Erno; Christopoulos, Athanasios; Laakso, Mikko-Jussi – Informatics in Education, 2023
Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student…
Descriptors: Prior Learning, Programming, Computer Science Education, Markov Processes
Li, Xiao; Xu, Hanchen; Zhang, Jinming; Chang, Hua-hua – Journal of Educational and Behavioral Statistics, 2023
The adaptive learning problem concerns how to create an individualized learning plan (also referred to as a learning policy) that chooses the most appropriate learning materials based on a learner's latent traits. In this article, we study an important yet less-addressed adaptive learning problem--one that assumes continuous latent traits.…
Descriptors: Learning Processes, Models, Algorithms, Individualized Instruction
Johnson, Marina E.; Misra, Ram; Berenson, Mark – Decision Sciences Journal of Innovative Education, 2022
In the era of artificial intelligence (AI), big data (BD), and digital transformation (DT), analytics students should gain the ability to solve business problems by integrating various methods. This teaching brief illustrates how two such methods--Bayesian analysis and Markov chains--can be combined to enhance student learning using the Analytics…
Descriptors: Bayesian Statistics, Programming Languages, Artificial Intelligence, Data Analysis
Kazu, Ibrahim Yasar; Kuvvetli, Murat – International Journal of Psychology and Educational Studies, 2023
Correct pronunciation significantly increases the intelligibility of communication. However, it is uncertain whether acquiring the pronunciation of the words enhances word retention capability. Therefore, the major purpose of this research is to evaluate whether vocabulary acquisition with the aid of pronouncing with artificial intelligence leads…
Descriptors: Artificial Intelligence, Teaching Methods, Pronunciation Instruction, High School Students
Shimmei, Machi; Matsuda, Noboru – International Educational Data Mining Society, 2020
One of the most challenging issues for online courseware engineering is to maintain the quality of instructional components, such as written text, video, and assessments. Learning engineers would like to know how individual instructional components contributed to students' learning. However, it is a hard task because it requires significant…
Descriptors: Teaching Methods, Engineering, Outcomes of Education, Courseware
du Boulay, Benedict; Luckin, Rosemary – International Journal of Artificial Intelligence in Education, 2016
Our original paper tried to characterize the richness of the teaching repertoire of expert human teachers and to give a sense of how far there still was to go in the development of pedagogic expertise in AIED systems. It considered three ways in which more expert teaching strategies and tactics might be developed. These were via (i) the…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Teaching Methods, Educational Strategies
Jain, G. Panka; Gurupur, Varadraj P.; Schroeder, Jennifer L.; Faulkenberry, Eileen D. – IEEE Transactions on Learning Technologies, 2014
In this paper, we describe a tool coined as artificial intelligence-based student learning evaluation tool (AISLE). The main purpose of this tool is to improve the use of artificial intelligence techniques in evaluating a student's understanding of a particular topic of study using concept maps. Here, we calculate the probability distribution of…
Descriptors: Artificial Intelligence, Concept Mapping, Teaching Methods, Student Evaluation
Stamper, John; Barnes, Tiffany; Croy, Marvin – International Journal of Artificial Intelligence in Education, 2011
The Hint Factory is an implementation of our novel method to automatically generate hints using past student data for a logic tutor. One disadvantage of the Hint Factory is the time needed to gather enough data on new problems in order to provide hints. In this paper we describe the use of expert sample solutions to "seed" the hint generation…
Descriptors: Cues, Prompting, Learning Strategies, Teaching Methods
Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis