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Khor, Ean Teng – International Journal of Information and Learning Technology, 2022
Purpose: The purpose of the study is to build predictive models for early detection of low-performing students and examine the factors that influence massive open online courses students' performance. Design/methodology/approach: For the first step, the author performed exploratory data analysis to analyze the dataset. The process was then…
Descriptors: Prediction, Low Achievement, Algorithms, Artificial Intelligence
Zanellati, Andrea; Macauda, Anita; Panciroli, Chiara; Gabbrielli, Maurizio – Research on Education and Media, 2023
Within scientific debate on post-digital and education, we present a position paper to describe a research project aimed at the design of a predictive model for students' low achievements in mathematics in Italy. The model is based on the INVALSI data set, an Italian large-scale assessment test, and we use decision trees as the classification…
Descriptors: Foreign Countries, Artificial Intelligence, Models, Algorithms
Hyun-Bin Hwang – Language Learning, 2025
This study explored the effects of practice schedule on the processing of new second language (L2) vocabulary and resulting knowledge. Participants were 107 low-achieving adolescents attending a vocational high school in Korea. They were randomly assigned to one of three practice groups and completed a L2 English-L1 Korean paired-associates…
Descriptors: Low Achievement, Adolescents, Second Language Learning, Vocabulary Development
Hu, Jie; Peng, Yi; Chen, Xiao – IEEE Transactions on Learning Technologies, 2023
The prevalence of information and communication technologies (ICTs) has brought about profound changes in the field of reading, resulting in a large and rapidly growing number of young digital readers. The article intends to identify key contextual factors that synergistically differentiate high and low performers, high and average performers, and…
Descriptors: Decoding (Reading), Educational Technology, Information Technology, Reading Skills

Robitaille, David F.; Sherrill, James M. – Alberta Journal of Educational Research, 1981
Data indicated that high percentages of fifth- through eighth-grade low achievers had high algorithm confidence for the operations of addition, subtraction, and multiplication. A substantial proportion in each grade expressed a low degree of confidence in their computational method for division. (CM)
Descriptors: Algorithms, Computation, Confidence Testing, Elementary Secondary Education
MacKay, Irene Douglas – 1975
The purpose of this study was to investigate the relationship between a student's confidence in his computational procedures for each of the four basic arithmetic operations and the student's achievement on computation problems. All of the students in grades 5 through 8 in one school system (a total of 6186 students) were given a questionnaire to…
Descriptors: Academic Achievement, Achievement, Algorithms, Computation
Feghali, Issa – 1976
A previous study had confirmed that a substantial number of low achievers in grades 5 through 8 had high algorithmic confidence in each of the four arithmetic operations with whole numbers. The purpose of the present study was to follow up the results through interviewing low achievement-high confidence students in order to ascertain if they…
Descriptors: Academic Achievement, Achievement, Algorithms, Computation
Gaslin, William L. – 1974
This paper reports the results of a ten-week study investigating the use of electronic calculators to perform basic operations with rational numbers in ninth-grade mathematics classes. In all, six classes and 101 students were involved. A short literature review is given and three treatments are described. Dependent variables were five mastery…
Descriptors: Algorithms, Grade 9, Low Ability Students, Low Achievement