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Pham Sy Nam; Ngoc-Giang Nguyen; Hoa Anh Tuong; Ben Haas; Zsolt Lavicza; Yves Kreis – International Journal for Technology in Mathematics Education, 2023
Problem-based learning puts students in situations that suggest problems without providing instructions and available knowledge. Therefore, when using problem-based learning, students need to be flexible, self-disciplined, active and self-occupied with knowledge and turn the knowledge the teacher intends to impart into their knowledge. For locus…
Descriptors: Computer Software, Mathematics Instruction, Teaching Methods, Problem Based Learning
Weisberg, Steven M.; Schinazi, Victor R.; Ferrario, Andrea; Newcombe, Nora S. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
Relying on shared tasks and stimuli to conduct research can enhance the replicability of findings and allow a community of researchers to collect large data sets across multiple experiments. This approach is particularly relevant for experiments in spatial navigation, which often require the development of unfamiliar large-scale virtual…
Descriptors: Programming, Error Patterns, Computer Simulation, Spatial Ability
Costello, Eamon; Johnston, Keith; Wade, Vincent – Interactive Learning Environments, 2023
This research investigated how the bug tracker database of the Virtual Learning Environment (VLE) Moodle is developed as an application of crowd work. The bug tracker is used by software developers, who write and maintain Moodle's code, but also by a wider public world of ordinary Moodle users who can report bugs. Despite many studies of the…
Descriptors: Electronic Learning, Educational Technology, Computer Software, Cooperation
Tsabari, Stav; Segal, Avi; Gal, Kobi – International Educational Data Mining Society, 2023
Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting "bug-fix-time", the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide…
Descriptors: College Students, Computer Science Education, Programming, Error Patterns
Daliri, Ayoub – Journal of Speech, Language, and Hearing Research, 2021
Purpose: The speech motor system uses feedforward and feedback control mechanisms that are both reliant on prediction errors. Here, we developed a state-space model to estimate the error sensitivity of the control systems. We examined (a) whether the model accounts for the error sensitivity of the control systems and (b) whether the two systems…
Descriptors: Speech Communication, Psychomotor Skills, Prediction, Error Patterns
Phoophuangpairoj, Rong; Pipattarasakul, Piyarat – International Journal of Educational Methodology, 2022
During the pandemic of Coronavirus disease 2019 (COVID-19), English as a foreign language (EFL) students have to study and submit their assignments and quizzes through online systems using electronic files instead of hardcopies. This has created an opportunity for teachers to use computer tools to conduct preliminary assessment of the students'…
Descriptors: Essays, Writing Evaluation, Second Language Learning, Second Language Instruction
Paul John; Nina Wolf – CALICO Journal, 2020
Our study examines written corrective feedback generated by two online grammar checkers (GCs), Grammarly and Virtual Writing Tutor, and by the grammar checking function of Microsoft Word. We tested the technology on a wide range of grammatical error types from two sources: a set of authentic ESL compositions and a series of simple sentences we…
Descriptors: English (Second Language), Feedback (Response), Automation, Grammar
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
Selami Aydin; Maryam Zeinolabedini – Online Submission, 2024
In line with the rapid advancement in educational technology, and the application of artificial intelligence (AI) in particular, the teaching and learning of the English language has undergone a significant transformation. This paper aims to explore students' perceptions of integrating AI into the English as a foreign language (EFL) learning…
Descriptors: Artificial Intelligence, Computer Software, Second Language Instruction, Second Language Learning
Xu, Yi – Interpreter and Translator Trainer, 2023
The research on interpreting aptitude has focused on the abilities, skills and personal traits of individuals in order to predict their future interpreting performance. However, an important variable between the personal characteristics and success of trainee interpreters in interpreter training, which is instructional practices, is overlooked.…
Descriptors: Prediction, Language Aptitude, Feedback (Response), Short Term Memory
Velez, Martin – ProQuest LLC, 2019
Software is an integral part of our lives. It controls the cars we drive every day, the ships we send into space, and even our toasters. It is everywhere and we can easily download more. Software solves many real-world problems and satisfies many needs. Thus, unsurprisingly, there is a rising demand for software engineers to maintain existing…
Descriptors: Computer Science Education, Programming, Introductory Courses, Computer Software
Mughaz, Dror; Cohen, Michael; Mejahez, Sagit; Ades, Tal; Bouhnik, Dan – Interdisciplinary Journal of e-Skills and Lifelong Learning, 2020
Aim/Purpose: Using Artificial Intelligence with Deep Learning (DL) techniques, which mimic the action of the brain, to improve a student's grammar learning process. Finding the subject of a sentence using DL, and learning, by way of this computer field, to analyze human learning processes and mistakes. In addition, showing Artificial Intelligence…
Descriptors: Artificial Intelligence, Teaching Methods, Brain Hemisphere Functions, Grammar
Brown, Neil C. C.; Altadmri, Amjad – ACM Transactions on Computing Education, 2017
Teaching is the process of conveying knowledge and skills to learners. It involves preventing misunderstandings or correcting misconceptions that learners have acquired. Thus, effective teaching relies on solid knowledge of the discipline, but also a good grasp of where learners are likely to trip up or misunderstand. In programming, there is much…
Descriptors: Novices, Programming Languages, Programming, Error Patterns
Bailey, Daniel; Lee, Andrea Rakushin – TESOL International Journal, 2020
Different genres of writing entail various levels of syntactic and lexical complexity, and how this complexity influences the results of Automatic Writing Evaluation (AWE) programs like Grammarly in second language (L2) writing is unknown. This study explored the use of Grammarly in the L2 writing context by comparing error frequency, error types…
Descriptors: Grammar, Computer Assisted Instruction, Error Correction, Feedback (Response)
Fredholm, Kent – Research-publishing.net, 2014
The use of online translation (OT) is increasing as more pupils receive laptops from their schools. This study investigates OT use in two groups of Swedish pupils (ages 17-18) studying Spanish as an L3: one group (A) having free Internet access and the spelling and grammar checker of Microsoft Word, the other group (B) using printed dictionaries…
Descriptors: Translation, Morphology (Languages), Accuracy, Questionnaires
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