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Eglington, Luke G.; Pavlik, Philip I., Jr. – International Journal of Artificial Intelligence in Education, 2023
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
Eglington, Luke G.; Pavlik, Philip I., Jr. – Grantee Submission, 2022
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
Woodworth, Johanathan; Barkaoui, Khaled – TESL Canada Journal, 2020
While feedback is widely considered essential for second language (L2) writing development (Bitchener & Ferris, 2012), teachers may not always be able to provide their learners with immediate and frequent corrective feedback. Automated writing evaluation (AWE) systems can help respond to this challenge by providing L2 learners with written…
Descriptors: Writing Evaluation, Feedback (Response), Error Correction, Second Language Instruction
Johnson, Douglas A.; Dickinson, Alyce M. – Psychological Record, 2012
Self-pacing, although often seen as one of the primary benefits of computer-based instruction (CBI), can also result in an important problem, namely, computer-based racing. Computer-based racing is when learners respond so quickly within CBI that mistakes are made, even on well-known material. This study compared traditional CBI with two forms of…
Descriptors: Computer Assisted Instruction, Scores, Feedback (Response), Comparative Analysis
Flanagan, Brendan; Yin, Chengjiu; Hirokawa, Sachio; Hashimoto, Kiyota; Tabata, Yoshiyuki – International Journal of Distance Education Technologies, 2013
In this paper, the entries of Lang-8, which is a Social Networking Site (SNS) site for learning and practicing foreign languages, were analyzed and found to contain similar rates of errors for most error categories reported in previous research. These similarly rated errors were then processed using an algorithm to determine corrections suggested…
Descriptors: Social Networks, Computer Assisted Instruction, Educational Technology, Second Language Instruction
Pyun, Danielle Ooyoung; Lee-Smith, Angela – Language, Culture and Curriculum, 2011
Heritage learners comprise a substantial proportion of second language learners, including L2 Korean learners. Previous studies have shown that the vast majority of heritage language learners display weakness in orthographic accuracy. Due to their previous extended exposure to oral discourse and limited experience in written language, heritage…
Descriptors: Feedback (Response), Verbal Communication, Spelling, Heritage Education
Trude, Alison M.; Tokowicz, Natasha – Language Learning, 2011
We examined negative transfer from English and Spanish to Portuguese pronunciation. Participants were native English speakers, some of whom spoke Spanish. Participants completed a computer-based Portuguese pronunciation tutorial and then pronounced trained letter-to-sound correspondences in unfamiliar Portuguese words; some shared orthographic…
Descriptors: Transfer of Training, Short Term Memory, Second Language Learning, Portuguese
Lee, Sun-Hee; Jang, Seok Bae; Seo, Sang-Kyu – CALICO Journal, 2009
In this study, we focus on particle errors and discuss an annotation scheme for Korean learner corpora that can be used to extract heuristic patterns of particle errors efficiently. We investigate different properties of particle errors so that they can be later used to identify learner errors automatically, and we provide resourceful annotation…
Descriptors: Feedback (Response), Error Patterns, Korean, Computational Linguistics

Hoko, J. Aaron; LeBlanc, Judith M. – Research in Developmental Disabilities, 1988
Because disabled learners may profit from procedures using gradual stimulus change, this study utilized a microcomputer to investigate the effectiveness of stimulus equalization, an error reduction procedure involving an abrupt but temporary reduction of dimensional complexity. The procedure was found to be generally effective and implications for…
Descriptors: Computer Assisted Instruction, Difficulty Level, Discrimination Learning, Error Patterns
Ninness, Chris; Rumph, Robin; McCuller, Glen; Harrison, Carol; Ford, Angela M.; Ninness, Sharon K. – Journal of Applied Behavior Analysis, 2005
Following a pretest, 11 participants who were naive with regard to various algebraic and trigonometric transformations received an introductory lecture regarding the fundamentals of the rectangular coordinate system. Following the lecture, they took part in a computer-interactive matching-to-sample procedure in which they received training on…
Descriptors: Computation, Graphs, Error Patterns, Algebra
Bright, George W. – Focus on Learning Problems in Mathematics, 1988
Considered are concept development and errors in using both Logo and BASIC made by 25 prospective teachers in a computer literacy course. Understanding errors that students exhibit in programing may affect the ways mathematics teachers understand the process of learning structured content like mathematics. (MNS)
Descriptors: College Students, Computer Assisted Instruction, Computer Literacy, Concept Formation
Rittle-Johnson, Bethany; Koedinger, Kenneth R. – Cognition and Instruction, 2005
We present a methodology for designing better learning environments. In Phase 1, 6th-grade students' (n = 223) prior knowledge was assessed using a difficulty factors assessment (DFA). The assessment revealed that scaffolds designed to elicit contextual, conceptual, or procedural knowledge each improved students' ability to add and subtract…
Descriptors: Prior Learning, Intervention, Mathematics Instruction, Problem Solving
Lewis, Matthew W. – 1980
This report describes an in-depth analysis of the errors made by users of SOLO, a programming language written for Open University students studying cognitive psychology. The study was designed to (1) determine the effectiveness of SOLO's current error-handling routines by evaluating how often SOLO produced "sensible" messages or…
Descriptors: Cognitive Processes, College Students, Computer Assisted Instruction, Computer Software

Hoppe, H. Ulrich – Journal of Artificial Intelligence in Education, 1994
Examines the deductive approach to error diagnosis for intelligent tutoring systems. Topics covered include the principles of the deductive approach to diagnosis; domain-specific heuristics to solve the problem of generalizing error patterns; and deductive diagnosis and the hypertext-based learning environment. (Contains 26 references.) (JLB)
Descriptors: Algorithms, Artificial Intelligence, Computer Assisted Instruction, Deduction
O'Connell, Ann Aileen; And Others – 1996
HyperProb is a Hypercard tutoring system designed to help students develop an effective step-by-step schema for solving probability problems. With this program, students are able to select areas they wish to study via hypermedia links and develop an understanding of terminology and procedures at their own pace with continued reinforcement. Nine…
Descriptors: Computer Assisted Instruction, Error Patterns, Formative Evaluation, Graduate Students
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