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De Backer, Liesje; Van Keer, Hilde; Valcke, Martin – Instructional Science: An International Journal of the Learning Sciences, 2015
Although successful collaborative learning requires socially shared metacognitive regulation (SSMR) of the learning process among multiple students, empirical research on SSMR is limited. The present study contributes to the emerging research on SSMR by examining its correlation with both collaborative learners' content processing strategies and…
Descriptors: Peer Teaching, Tutoring, Metacognition, Cooperative Learning
Klingler, Severin; Käser, Tanja; Solenthaler, Barbara; Gross, Markus – International Educational Data Mining Society, 2015
Modeling student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for modeling the acquisition of knowledge is Bayesian Knowledge Tracing (BKT). Various extensions to the original BKT model have been proposed, among them two novel models that unify BKT and Item Response Theory (IRT). Latent Factor Knowledge…
Descriptors: Intelligent Tutoring Systems, Knowledge Level, Item Response Theory, Prediction
Van Inwegen, Eric G.; Adjei, Seth A.; Wang, Yan; Heffernan, Neil T. – International Educational Data Mining Society, 2015
User modelling algorithms such as Performance Factors Analysis and Knowledge Tracing seek to determine a student's knowledge state by analyzing (among other features) right and wrong answers. Anyone who has ever graded an assignment by hand knows that some answers are "more wrong" than others; i.e. they display less of an understanding…
Descriptors: Knowledge Level, Performance Factors, Error Patterns, Mathematics
Matthew E. Jacovina; Erica L. Snow; G. Tanner Jackson; Danielle S. McNamara – Grantee Submission, 2015
To optimize the benefits of game-based practice within Intelligent Tutoring Systems (ITSs), researchers examine how game features influence students' motivation and performance. The current study examined the influence of game features and individual differences (reading ability and learning intentions) on motivation and performance. Participants…
Descriptors: Game Based Learning, Intelligent Tutoring Systems, Learning Motivation, Performance
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Lee, Nancy; Hong, Eunsook – IAFOR Journal of Education, 2017
The study described here explored the differential effects of two learning strategies, self-explanation and reading questions and answers, on learning the computer programming language JavaScript. Students' test performance and perceptions of effectiveness toward the two strategies were examined. An online interactive tutorial instruction…
Descriptors: Computer Science Education, Programming, Introductory Courses, High School Students
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Spark, Linda; De Klerk, Danie; Maleswena, Tshepiso; Jones, Andrew – Journal of Student Affairs in Africa, 2017
The exponential growth of higher education enrolment in South Africa has resulted in increased diversity of the student body, leading to a proliferation of factors that affect student performance and success. Various initiatives have been adopted by tertiary institutions to mitigate the negative impact these factors may have on student success,…
Descriptors: Success, Teaching Methods, Curriculum Implementation, Tutoring
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Wegner, Theresa Marie – International Journal of Adult Vocational Education and Technology, 2017
This qualitative study identified the factors that contributed to the success experienced by students with learning disabilities in their first year of college. The primary factors that emerged from student interviews were their attitudes about higher education, and their personal attributes including motivation, maturity, and persistence.…
Descriptors: Learning Disabilities, Self Determination, Qualitative Research, College Freshmen
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Falk-Ross, Francine; Dealy, Ann; Porcelli, Justinna; Hammond, Jonna; Evans, Brian – Reading & Writing Quarterly, 2017
We studied the process and products of developing effective tutoring programs to support Tier 2 bilingual students with a focus on teachers' perspectives and students' achievement to assess and teach students based on need. With the understanding that young students require individual attention to resolve specific misunderstandings, optimally…
Descriptors: Bilingualism, After School Programs, Tutoring, Literacy Education
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Xin, Yan Ping; Tzur, Ron; Hord, Casey; Liu, Jia; Park, Joo Young; Si, Luo – Learning Disability Quarterly, 2017
The Common Core Mathematics Standards have raised expectations for schools and students in the United States. These standards demand much deeper content knowledge from teachers of mathematics and their students. Given the increasingly diverse student population in today's classrooms and shortage of qualified special education teachers,…
Descriptors: Intelligent Tutoring Systems, Computer Assisted Instruction, Mathematics Instruction, Learning Disabilities
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Michalenko, Joshua J.; Lan, Andrew S.; Waters, Andrew E.; Grimaldi, Philip J.; Baraniuk, Richard G. – International Educational Data Mining Society, 2017
An important, yet largely unstudied problem in student data analysis is to detect "misconceptions" from students' responses to "open-response" questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality of…
Descriptors: Data Analysis, Misconceptions, Student Attitudes, Feedback (Response)
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Xu, Wei; Zhao, Ke; Li, Yatao; Yi, Zhenzhen – International Journal of Distance Education Technologies, 2012
Determining how to provide good tutoring functions is an important research direction of intelligent tutoring systems. In this study, the authors develop an intelligent tutoring system with good tutoring functions, called "FUDAOWANG." The research domain that FUDAOWANG treats is junior middle school mathematics, which belongs to the objective…
Descriptors: Tutoring, Intelligent Tutoring Systems, Computer Software, Problem Solving
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D'Mello, S. K.; Graesser, A. – IEEE Transactions on Learning Technologies, 2012
We explored the possibility of predicting student emotions (boredom, flow/engagement, confusion, and frustration) by analyzing the text of student and tutor dialogues during interactions with an Intelligent Tutoring System (ITS) with conversational dialogues. After completing a learning session with the tutor, student emotions were judged by the…
Descriptors: Tutoring, Intelligent Tutoring Systems, Psychological Patterns, Prediction
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Toms, Marcia – Learning Assistance Review, 2016
The material in this article is a compilation of the results of an National College Learning Center Association (NCLCA) study conducted by Dr. Marcia Toms under the auspices of NC State University which came from 211 unique institutions during the Spring of 2014. Invitations to complete the survey were sent to all past and present NCLCA members as…
Descriptors: Surveys, Questionnaires, Benchmarking, Geographic Regions
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Rogers, Rebecca; Mosley Wetzel, Melissa; O'Daniels, Katherine – Pedagogies: An International Journal, 2016
Critical literacy requires an exploration of privilege and social justice. This includes an exploration of power and action in one's "inner" and "outer" lives. This qualitative case study illustrates the ways in which Jonah, a preservice teacher, navigates social practices and actions in his roles as a student, activist, and…
Descriptors: Critical Literacy, Qualitative Research, Case Studies, Preservice Teachers
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Hooshyar, Danial; Ahmad, Rodina Binti; Yousefi, Moslem; Fathi, Moein; Abdollahi, Abbas; Horng, Shi-Jinn; Lim, Heuiseok – Educational Technology Research and Development, 2016
Nowadays, intelligent tutoring systems are considered an effective research tool for learning systems and problem-solving skill improvement. Nonetheless, such individualized systems may cause students to lose learning motivation when interaction and timely guidance are lacking. In order to address this problem, a solution-based intelligent…
Descriptors: Intelligent Tutoring Systems, Technology Integration, Educational Games, Formative Evaluation
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