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Steedle, Jeffrey T.; Cho, Young Woo; Wang, Shichao; Arthur, Ann M.; Li, Dongmei – Educational Measurement: Issues and Practice, 2022
As testing programs transition from paper to online testing, they must study mode comparability to support the exchangeability of scores from different testing modes. To that end, a series of three mode comparability studies was conducted during the 2019-2020 academic year with examinees randomly assigned to take the ACT college admissions exam on…
Descriptors: College Entrance Examinations, Computer Assisted Testing, Scores, Test Format
Sefcik, Lesley; Veeran-Colton, Terisha; Baird, Michael; Price, Connie; Steyn, Steve – Australasian Journal of Educational Technology, 2022
This study aimed to understand the effects of a custom-developed, artificial intelligence-based, asynchronous remote invigilation system on the student user experience. The study was conducted over 3 years at a large Australian university, and findings demonstrate that familiarity with the system over time improved student attitudes towards remote…
Descriptors: Usability, Users (Information), Student Attitudes, Supervision
Al-Jarf, Reima – Online Submission, 2022
This study investigates grade inflation in 127 language, linguistics, translation, education, and computer courses taught at some Saudi universities before, during and after the Pandemic. Grades obtained from some instructors for courses taught over 8 semesters were analyzed. It was found that between 20% 65% chose a pass/no-grade results, the…
Descriptors: Grade Inflation, Universities, Foreign Countries, COVID-19
Sylla, Khalifa; Babou, Birahim; Ouya, Samuel – International Association for Development of the Information Society, 2022
This paper deals with a solution allowing digital universities to extend the functionalities of their distance learning platform to offer a secure solution for the dematerialization of assessments. Currently we are witnessing the rise of digital universities, this is the case in Africa, particularly in Senegal. We are witnessing strong growth in…
Descriptors: Foreign Countries, Virtual Universities, Computer Assisted Testing, Educational Technology
Andrea Cedeno – ProQuest LLC, 2022
The relationship between computer-based and paper-based testing may vary. Students with special needs may or may not perform better on computer-based testing compared to paper-based testing. Over the past few decades, computers and technology have increased in society and in classrooms. The use of technology has increased during the era of the…
Descriptors: Computer Assisted Testing, Test Format, Special Needs Students, Reading Comprehension
Edwin Ambrosio – ProQuest LLC, 2022
Assessments are some of the most common tools used to evaluate student learning. While exams have always been a part of evaluating how well students learn and retain information, the most effective way to administer them has always been debated. However, remarkably few studies have compared online and paper testing, and even fewer have examined…
Descriptors: Computer Science Education, Computer Assisted Testing, Test Format, Performance
Binglin Chen – ProQuest LLC, 2022
Assessment is a key component of education. Routine grading of students' work, however, is time consuming. Automating the grading process allows instructors to spend more of their time helping their students learn and engaging their students with more open-ended, creative activities. One way to automate grading is through computer-based…
Descriptors: College Students, STEM Education, Student Evaluation, Grading
Fergadiotis, Gerasimos; Hula, William D.; Swiderski, Alexander M.; Lei, Chia-Ming; Stacey Kellough – Journal of Speech, Language, and Hearing Research, 2019
Purpose: In this study, we investigated the agreement between the 175-item Philadelphia Naming Test (PNT; Roach, Schwartz, Martin, Grewal, & Brecher, 1996) and a 30-item computer adaptive PNT (PNT-CAT; Fergadiotis, Kellough, & Hula, 2015; Hula, Kellough, & Fergadiotis, 2015) created using item response theory (IRT) methods. Method: The…
Descriptors: Computer Assisted Testing, Aphasia, Naming, Test Validity
Zhang, Mengxue; Heffernan, Neil; Lan, Andrew – International Educational Data Mining Society, 2023
Automated scoring of student responses to open-ended questions, including short-answer questions, has great potential to scale to a large number of responses. Recent approaches for automated scoring rely on supervised learning, i.e., training classifiers or fine-tuning language models on a small number of responses with human-provided score…
Descriptors: Scoring, Computer Assisted Testing, Mathematics Instruction, Mathematics Tests
Ritonga, Mahyudin; Shaban, Ahmad Abdulkareem; Al-Rashidi, Anwar Hammad; Chilani, Noam – Language Testing in Asia, 2023
Every decision made in the classroom has the potential to either help or hurt a student's development as a learner. When students' mental and emotional well-being are taken into account, effective instruction and assessment are possible, despite the importance of learners' engagement in assessment (LEA), test-taking skills (TTS), self-assessment…
Descriptors: Learner Engagement, Student Evaluation, Test Wiseness, Resilience (Psychology)
Zheng, Lanqin; Long, Miaolang; Chen, Bodong; Fan, Yunchao – International Journal of Educational Technology in Higher Education, 2023
Online collaborative learning is implemented extensively in higher education. Nevertheless, it remains challenging to help learners achieve high-level group performance, knowledge elaboration, and socially shared regulation in online collaborative learning. To cope with these challenges, this study proposes and evaluates a novel automated…
Descriptors: Learning Analytics, Computer Assisted Testing, Cooperative Learning, Graphs
Yildirim-Erbasli, Seyma N.; Bulut, Okan; Demmans Epp, Carrie; Cui, Ying – Journal of Educational Technology Systems, 2023
Conversational agents have been widely used in education to support student learning. There have been recent attempts to design and use conversational agents to conduct assessments (i.e., conversation-based assessments: CBA). In this study, we developed CBA with constructed and selected-response tests using Rasa--an artificial intelligence-based…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Computer Mediated Communication, Formative Evaluation
Nikolic, Sasha; Daniel, Scott; Haque, Rezwanul; Belkina, Marina; Hassan, Ghulam M.; Grundy, Sarah; Lyden, Sarah; Neal, Peter; Sandison, Caz – European Journal of Engineering Education, 2023
ChatGPT, a sophisticated online chatbot, sent shockwaves through many sectors once reports filtered through that it could pass exams. In higher education, it has raised many questions about the authenticity of assessment and challenges in detecting plagiarism. Amongst the resulting frenetic hubbub, hints of potential opportunities in how ChatGPT…
Descriptors: Artificial Intelligence, Performance Based Assessment, Engineering Education, Integrity
Escudero, Paola; Smit, Eline A.; Angwin, Anthony J. – Language Learning, 2023
Research has shown that novel words can be learned through the mechanism of statistical or cross-situational word learning (CSWL). So far, CSWL studies using adult populations have focused on the presentation of spoken words. However, words can also be learned through their written form. This study compared auditory and orthographic presentations…
Descriptors: Word Lists, Vocabulary Development, Comparative Analysis, Auditory Stimuli
Madugula, Neelima; Allamsetty, Srikanth; Sinha, Abhinav; Mishra, Subhendu; Chandra, M. V. Satya Sai; Krishnan, Dhanya – Journal of Applied Research in Higher Education, 2023
Purpose: With the current COVID-19 outbreak, the majority of the higher educational institutes (HEI) are conducting online examinations to assess their students, where both teachers and students are facing various constraints. Under these circumstances, it is very important to understand the perspectives of teachers and students towards online…
Descriptors: Teacher Attitudes, Student Attitudes, COVID-19, Pandemics

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