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Yang, Albert C. M.; Chen, Irene Y. L.; Flanagan, Brendan; Ogata, Hiroaki – Educational Technology & Society, 2021
Precision education is a new challenge in leveraging artificial intelligence, machine learning, and learning analytics to enhance teaching quality and learning performance. To facilitate precision education, text marking skills can be used to determine students' learning process. Text marking is an essential learning skill in reading. In this…
Descriptors: Grading, Computer Assisted Testing, Automation, Artificial Intelligence
Frost, Kellie; Wigglesworth, Gillian; Clothier, Josh – Language Assessment Quarterly, 2021
The use of integrated tasks to test English-speaking skills raises questions about the impact of comprehension on test score outcomes, and the impact of stimulus materials on test-taker strategic behaviours. This study analysed speaking performances and verbal report data to examine the strategies used by test takers at different levels of…
Descriptors: Task Analysis, Second Language Learning, English (Second Language), Language Tests
Zimbardi, Kirsten; Colthorpe, Kay; Dekker, Andrew; Engstrom, Craig; Bugarcic, Andrea; Worthy, Peter; Victor, Ruban; Chunduri, Prasad; Lluka, Lesley; Long, Phil – Assessment & Evaluation in Higher Education, 2017
Feedback is known to have a large influence on student learning gains, and the emergence of online tools has greatly enhanced the opportunity for delivering timely, expressive, digital feedback and for investigating its learning impacts. However, to date there have been no large quantitative investigations of the feedback provided by large teams…
Descriptors: Student Evaluation, Feedback (Response), Academic Achievement, Achievement Gains
Barrett, Matthew E.; Swan, Alexander B.; Mamikonian, Ani; Ghajoyan, Inna; Kramarova, Olga; Youmans, Robert J. – International Journal of Instruction, 2014
This study examined the encoding specificity principle in relation to traditional and computer-based note taking and assessment formats in higher education. Students (N = 79) took lecture notes either by hand (n = 40) or by computer (n = 39) and then completed either a computer or a paper-based assessment. When note taking and assessment formats…
Descriptors: Notetaking, Congruence (Psychology), Higher Education, College Students
Carrell, Patricia L.; Dunkel, Patricia A.; Mollaun, Pamela – Applied Language Learning, 2004
With the goal of improving the reliability and validity of tests of English as a foreign language, in particular with respect to the question of whether to allow notetaking on EFL/ESL computer-based listening comprehension tests, the present study examined the effects on ESL listening comprehension of notetaking (allowed or disallowed) in relation…
Descriptors: English (Second Language), Listening Comprehension Tests, Computer Assisted Testing, Notetaking
Carrell, Patricia L. – ETS Research Report Series, 2007
Utilizing a pre- and posttest research design, with an instructional intervention of good practices in notetaking, the notes taken by examinees during a computer-based listening comprehension test prior to and following the instructional intervention were examined for particular notetaking strategies. Questionnaires probed perceptions of the…
Descriptors: Notetaking, Pretests Posttests, Correlation, English (Second Language)
Henderson, George; Nash, Susan Smith – Charles C. Thomas, Publisher, Ltd, 2007
This book will improve the quality of instruction that college students need. It makes numerous suggestions that must be tended to when teachers instruct students. For example, the authors speculate about ways teachers can present what may at times seem to be a mountain of information without burying students under it; why teachers must…
Descriptors: Notetaking, Short Term Memory, Internet, Discussion Groups

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