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Zhiwen Tang – ProQuest LLC, 2021
Artificial intelligence (AI) aims to build intelligent systems that can interact with and assist humans. During the interaction, a system learns the requirements from the human user and adapts to the needs to complete tasks. A popular type of interactive system is retrieval-based, where the system uses a retrieval function to retrieve relevant…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Objectives, Reinforcement
Claudia Lizette Garay-Rondero; Alvaro Castillo-Paz; Carlos Gijón-Rivera; Gerardo Domínguez-Ramírez; Conrado Rosales-Torres; Alberto Oliart-Ros – Cogent Education, 2024
Higher Education faces challenges in providing learning experiences and cultivating competencies in our modern environment. Academic discourse centres on tutoring automation, a complicated educational dilemma in the age of Machine Learning, Deep Learning, and Artificial Intelligence. Establishing a structured technique for real competency…
Descriptors: Competency Based Education, Performance Based Assessment, Evaluation Methods, Undergraduate Students
Danial Hooshyar; Nour El Mawas; Yeongwook Yang – Knowledge Management & E-Learning, 2024
The use of learner modelling approaches is critical for providing adaptive support in educational computer games, with predictive learner modelling being among the key approaches. While adaptive supports have been shown to improve the effectiveness of educational games, improperly customized support can have negative effects on learning outcomes.…
Descriptors: Artificial Intelligence, Course Content, Tests, Scores
How, Meng-Leong; Hung, Wei Loong David – Education Sciences, 2019
Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved…
Descriptors: Stakeholders, Artificial Intelligence, Bayesian Statistics, Probability
Davy Tsz Kit Ng; Jiahong Su; Jac Ka Lok Leung; Samuel Kai Wah Chu – Interactive Learning Environments, 2024
Artificial intelligence (AI) literacy has emerged to equip students with digital skills for effective evaluation, communication, collaboration, and ethical use of AI in online, home, and workplace settings. Countries are increasingly developing AI curricula to support students' technological skills for future studies and careers. However, there is…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Secondary School Students
Surubaru, Teodora; Isoc, Dorin – International Association for Development of the Information Society, 2019
The requirement to assure the teaching of critical thinking put the school in front of its own weaknesses. A profound criticism highlights limitations, hindrances and obstacles that are difficult to pass without the personal efforts of the teachers. Following criticism, one can identify a set of requirements that would allow for improvement and…
Descriptors: Critical Thinking, Teaching Methods, Barriers, Intervention
Chin, Doris B.; Blair, Kristen P.; Wolf, Rachel C.; Conlin, Luke D.; Cutumisu, Maria; Pfaffman, Jay; Schwartz, Daniel L. – Journal of the Learning Sciences, 2019
Educators aim to equip students with learning strategies they can apply when approaching new problems on their own. Teaching design-thinking strategies may support this goal. A first test would show that the strategies are good for learning and that students spontaneously transfer them beyond classroom instruction. To examine this, we introduce…
Descriptors: Problem Solving, Learning Strategies, Teaching Methods, Design
Lavbic, Dejan; Matek, Tadej; Zrnec, Aljaž – Interactive Learning Environments, 2017
Today's software industry requires individuals who are proficient in as many programming languages as possible. Structured query language (SQL), as an adopted standard, is no exception, as it is the most widely used query language to retrieve and manipulate data. However, the process of learning SQL turns out to be challenging. The need for a…
Descriptors: Evaluation Methods, Information Systems, Intelligent Tutoring Systems, Computer Science Education
Doroudi, Shayan; Holstein, Kenneth; Aleven, Vincent; Brunskill, Emma – Grantee Submission, 2016
How should a wide variety of educational activities be sequenced to maximize student learning? Although some experimental studies have addressed this question, educational data mining methods may be able to evaluate a wider range of possibilities and better handle many simultaneous sequencing constraints. We introduce Sequencing Constraint…
Descriptors: Sequential Learning, Data Collection, Information Retrieval, Evaluation Methods
Anderson, John R. – Neuropsychologia, 2012
Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…
Descriptors: Markov Processes, Intelligent Tutoring Systems, Problem Solving, Methods
Baker, Ryan S.; Hershkovitz, Arnon; Rossi, Lisa M.; Goldstein, Adam B.; Gowda, Sujith M. – Journal of the Learning Sciences, 2013
We present a new method for analyzing a student's learning over time for a specific skill: analysis of the graph of the student's moment-by-moment learning over time. Moment-by-moment learning is calculated using a data-mined model that assesses the probability that a student learned a skill or concept at a specific time during learning (Baker,…
Descriptors: Learning Processes, Intelligent Tutoring Systems, Probability, Skill Development
Boyer, Kristy Elizabeth, Ed.; Yudelson, Michael, Ed. – International Educational Data Mining Society, 2018
The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…
Descriptors: Data Collection, Data Analysis, Computer Science Education, Program Proposals
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
Pulz, Michael; Lusti, Markus – 1994
PROJECTTUTOR is an intelligent tutoring system that enhances conventional classroom instruction by teaching problem solving in project planning. The domain knowledge covered by the expert module is divided into three functions. Structural analysis, identifies the activities that make up the project, time analysis, computes the earliest and latest…
Descriptors: Authoring Aids (Programming), Case Studies, Computer Assisted Instruction, Educational Technology
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
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