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Direct linkHaldeman, Georgiana; Babes-Vroman Monica; Tjang, Andrew; Nguyen, Thu D. – ACM Transactions on Computing Education, 2021
Autograding systems are being increasingly deployed to meet the challenges of teaching programming at scale. Studies show that formative feedback can greatly help novices learn programming. This work extends an autograder, enabling it to provide formative feedback on programming assignment submissions. Our methodology starts with the design of a…
Descriptors: Student Evaluation, Feedback (Response), Grading, Automation
Rafferty, Anna N.; Jansen, Rachel A.; Griffiths, Thomas L. – Cognitive Science, 2020
Online educational technologies offer opportunities for providing individualized feedback and detailed profiles of students' skills. Yet many technologies for mathematics education assess students based only on the correctness of either their final answers or responses to individual steps. In contrast, examining the choices students make for how…
Descriptors: Computer Assisted Testing, Mathematics Tests, Mathematics Skills, Student Evaluation
Gauns Dessai, Kissan G.; Kamat, Venkatesh V. – International Journal of Information and Communication Technology Education, 2018
Educational institutions worldwide conduct summative examinations to evaluate academic performance of students. Such summative examinations are normally subjective in nature in higher education institutions and needs manual evaluation. However, the manual evaluation of subjective answer-scripts often suffers from evaluation anomalies and the…
Descriptors: Computer Assisted Testing, Student Evaluation, Scoring Rubrics, Error Patterns
Download full textGanzfried, Sam; Yusuf, Farzana – Education Sciences, 2018
A problem faced by many instructors is that of designing exams that accurately assess the abilities of the students. Typically, these exams are prepared several days in advance, and generic question scores are used based on rough approximation of the question difficulty and length. For example, for a recent class taught by the author, there were…
Descriptors: Weighted Scores, Test Construction, Student Evaluation, Multiple Choice Tests
Steif, Paul S.; Fu, Luoting; Kara, Levent Burak – Interactive Learning Environments, 2016
Problems faced by engineering students involve multiple pathways to solution. Students rarely receive effective formative feedback on handwritten homework. This paper examines the potential for computer-based formative assessment of student solutions to multipath engineering problems. In particular, an intelligent tutor approach is adopted and…
Descriptors: Formative Evaluation, Engineering Education, Problem Solving, Intelligent Tutoring Systems
Jiang, Yaoyi – British Journal of Educational Technology, 2015
The Report of Chinese Students' English Writing Ability (2014) focuses on the Chinese students' English writing in the automated essay-evaluation context. The data and samples are primarily from a national-wide writing project involving 300,814 English as a Foreign Language participants from 452 schools in China during a period of April 10 to May…
Descriptors: Foreign Countries, Writing Ability, Essays, English (Second Language)
Blanchard, Daniel; Tetreault, Joel; Higgins, Derrick; Cahill, Aoife; Chodorow, Martin – ETS Research Report Series, 2013
This report presents work on the development of a new corpus of non-native English writing. It will be useful for the task of native language identification, as well as grammatical error detection and correction, and automatic essay scoring. In this report, the corpus is described in detail.
Descriptors: Language Tests, Second Language Learning, English (Second Language), Writing Tests
Yoon, Su-Youn – ProQuest LLC, 2009
This dissertation provides an automated scoring method of speech fluency for second language learners of English (L2 learners) based that uses speech recognition technology. Non-standard pronunciation, frequent disfluencies, faulty grammar, and inappropriate lexical choices are crucial characteristics of L2 learners' speech. Due to the ease of…
Descriptors: Phonemes, Second Language Learning, Scoring, Correlation
Peer reviewedOrey, Michael A.; Burton, John K. – Journal of Research on Computing in Education, 1992
Describes a study that was conducted to determine how error patterns and a clinical approach to assessing third grade students' mathematical knowledge interrelate to design a computer diagnostic system for subtraction. Error patterns are examined, and computer diagnostic systems and analytical testing are discussed. (34 references) (LRW)
Descriptors: Computer Assisted Testing, Computer System Design, Elementary Education, Error Patterns
Hasselbring, Ted S.; And Others – 1989
This monograph provides an overview of computer-based assessment and error analysis in the instruction of elementary students with complex medical, learning, and/or behavioral problems. Information on generating and scoring tests using the microcomputer is offered, as are ideas for using computers in the analysis of mathematical strategies and…
Descriptors: Behavior Problems, Computer Assisted Testing, Computer Managed Instruction, Diagnostic Teaching
Tatsuoka, Kikumi K. – 1985
This paper introduces a probabilistic model that is capable of diagnosing and classifying cognitive errors in a general problem-solving domain. Item response theory is used to deal with the variability of response errors. Responses from a 38 item fraction addition test given to 595 junior high school students are used to illustrate the model.…
Descriptors: Artificial Intelligence, Cognitive Processes, Computer Assisted Testing, Computer Software
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection
