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Haldeman, 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
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Granena, Gisela; Yilmaz, Yucel – Language Learning, 2019
This study investigated the relative effectiveness of two instructional interventions (implicit and explicit feedback) as a function of implicit sequence-learning ability. Second language (L2) attainment was measured by means of a self-paced reading task, which shows online sensitivity to language errors. Implicit sequence-learning ability was…
Descriptors: Error Correction, Feedback (Response), Instructional Effectiveness, Intervention
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Nutbrown, Stephen; Higgins, Colin – Computer Science Education, 2016
This article explores the suitability of static analysis techniques based on the abstract syntax tree (AST) for the automated assessment of early/mid degree level programming. Focus is on fairness, timeliness and consistency of grades and feedback. Following investigation into manual marking practises, including a survey of markers, the assessment…
Descriptors: Programming, Grading, Evaluation Methods, Feedback (Response)
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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
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Mao, Liyang; Liu, Ou Lydia; Roohr, Katrina; Belur, Vinetha; Mulholland, Matthew; Lee, Hee-Sun; Pallant, Amy – Educational Assessment, 2018
Scientific argumentation is one of the core practices for teachers to implement in science classrooms. We developed a computer-based formative assessment to support students' construction and revision of scientific arguments. The assessment is built upon automated scoring of students' arguments and provides feedback to students and teachers.…
Descriptors: Computer Assisted Testing, Science Tests, Scoring, Automation
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Li, Zhi; Feng, Hui-Hsien; Saricaoglu, Aysel – CALICO Journal, 2017
This classroom-based study employs a mixed-methods approach to exploring both short-term and long-term effects of Criterion feedback on ESL students' development of grammatical accuracy. The results of multilevel growth modeling indicate that Criterion feedback helps students in both intermediate-high and advanced-low levels reduce errors in eight…
Descriptors: Feedback (Response), Criterion Referenced Tests, Computer Assisted Testing, Writing Tests
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Walker, Philip; Gwynllyw, D. Rhys; Henderson, Karen L. – Teaching Mathematics and Its Applications, 2015
We demonstrate how the re-marker and reporter facility of the DEWIS e-Assessment system facilitates the capture, analysis and reporting of student errors using two case studies: logarithms and indices for first-year computing students at the University of the West of England, and Sturm-Liouville problems for second-year mathematics students at…
Descriptors: Computer Assisted Testing, Error Patterns, Case Studies, College Mathematics
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Pelánek, Radek; Rihák, Ji?rí – International Educational Data Mining Society, 2016
In online educational systems we can easily collect and analyze extensive data about student learning. Current practice, however, focuses only on some aspects of these data, particularly on correctness of students answers. When a student answers incorrectly, the submitted wrong answer can give us valuable information. We provide an overview of…
Descriptors: Foreign Countries, Online Systems, Geography, Anatomy
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Ha, Minsu; Nehm, Ross H. – Journal of Science Education and Technology, 2016
Automated computerized scoring systems (ACSSs) are being increasingly used to analyze text in many educational settings. Nevertheless, the impact of misspelled words (MSW) on scoring accuracy remains to be investigated in many domains, particularly jargon-rich disciplines such as the life sciences. Empirical studies confirm that MSW are a…
Descriptors: Spelling, Case Studies, Computer Uses in Education, Test Scoring Machines
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English, John; English, Tammy – Journal of Information Technology Education: Innovations in Practice, 2015
In this paper we discuss the use of automated assessment in a variety of computer science courses that have been taught at Israel Academic College by the authors. The course assignments were assessed entirely automatically using Checkpoint, a web-based automated assessment framework. The assignments all used free-text questions (where the students…
Descriptors: Computer Science Education, Computer Assisted Testing, Foreign Countries, College Students
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Feng, Mingyu, Ed.; Käser, Tanja, Ed.; Talukdar, Partha, Ed. – International Educational Data Mining Society, 2023
The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…
Descriptors: Information Retrieval, Data Analysis, Computer Assisted Testing, Cheating
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