NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Laws, Policies, & Programs
Assessments and Surveys
Program for International…1
What Works Clearinghouse Rating
Showing all 9 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Nayak, Padmalaya; Vaheed, Sk.; Gupta, Surbhi; Mohan, Neeraj – Education and Information Technologies, 2023
Students' academic performance prediction is one of the most important applications of Educational Data Mining (EDM) that helps to improve the quality of the education process. The attainment of student outcomes in an Outcome-based Education (OBE) system adds invaluable rewards to facilitate corrective measures to the learning processes.…
Descriptors: Predictor Variables, Academic Achievement, Data Collection, Information Retrieval
Peer reviewed Peer reviewed
Direct linkDirect link
Cechinel, Cristian; Ochoa, Xavier; Lemos dos Santos, Henrique; Carvalho Nunes, João Batista; Rodés, Virginia; Marques Queiroga, Emanuel – British Journal of Educational Technology, 2020
The growth of Learning Analytics (LA) as a research field has been extensively documented since its beginnings. This paper provides a broad overview of the publications that Latin American authors have published in the last years by performing a quantitative review of the literature (from 2011 to 2019). A total of 282 papers were collected and…
Descriptors: Data Analysis, Authors, Foreign Countries, Ethics
Peer reviewed Peer reviewed
Direct linkDirect link
Smith, Marlene A.; Kellogg, Deborah L. – Decision Sciences Journal of Innovative Education, 2015
This article describes a predictive model that assesses whether a student will have greater perceived learning in group assignments or in individual work. The model produces correct classifications 87.5% of the time. The research is notable in that it is the first in the education literature to adopt a predictive modeling methodology using data…
Descriptors: Group Activities, Assignments, Cooperative Learning, Individual Activities
Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam – International Educational Data Mining Society, 2015
This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features…
Descriptors: Learning Activities, Learning Processes, Data Collection, Student Behavior
Peer reviewed Peer reviewed
Direct linkDirect link
Martin, Taylor; Sherin, Bruce – Journal of the Learning Sciences, 2013
The learning sciences community's interest in learning analytics (LA) has been growing steadily over the past several years. Three recent symposia on the theme (at the American Educational Research Association 2011 and 2012 annual conferences, and the International Conference of the Learning Sciences 2012), organized by Paulo Blikstein, led…
Descriptors: Data Analysis, Learning Processes, Educational Research, Data Collection
Peer reviewed Peer reviewed
Direct linkDirect link
Patterson, Jeffery; Merwin, Brandy J. – Science Teacher, 2002
Learning cycle investigations allow science students to model the activities of real scientists. An important step in modeling is data collection, which can present a problem in an astronomy course. The celestial body being studied often is unobservable for various reasons. (Contains 3 figures.)
Descriptors: Astronomy, Classification, Learning Processes, Investigations
Tobin, Kenneth G.; Capie, William – 1981
This paper advocates categorization of engagement on the basis of a logical relationship with the outcomes of a study and the use of student attributes that are logically related to engagement and/or achievement as covariables. Results from a study involving nine engagement categories, measures of formal reasoning ability, locus of control and…
Descriptors: Academic Achievement, Classification, Classroom Research, Data Collection
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
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers