NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 8 results Save | Export
Jennifer Hill; George Perrett; Vincent Dorie – Grantee Submission, 2023
Estimation of causal effects requires making comparisons across groups of observations exposed and not exposed to a a treatment or cause (intervention, program, drug, etc). To interpret differences between groups causally we need to ensure that they have been constructed in such a way that the comparisons are "fair." This can be…
Descriptors: Causal Models, Statistical Inference, Artificial Intelligence, Data Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Liu, Yi; Xu, TianWei; Xiao, Mengjin – International Journal of Information and Communication Technology Education, 2023
In order to better grasp the needs of library users and provide them with more accurate knowledge services, combining the characteristics of university libraries, this article applies library small data to personalized recommendation and proposes a small data fusion algorithm model for library personalized recommendation. This model combines the…
Descriptors: Research Libraries, Data Collection, Data Analysis, Tables (Data)
Peer reviewed Peer reviewed
Direct linkDirect link
David Burlinson; Matthew Mcquaigue; Alec Goncharow; Kalpathi Subramanian; Erik Saule; Jamie Payton; Paula Goolkasian – Education and Information Technologies, 2024
BRIDGES is a software framework for creating engaging assignments for required courses such as data structures and algorithms. It provides students with a simplified API that populates their own data structure implementations with live and real-world data, and provides the ability for students to easily visualize the data structures they create as…
Descriptors: Computer Science Education, Majors (Students), Student Interests, College Faculty
Peer reviewed Peer reviewed
Direct linkDirect link
Parhizkar, Amirmohammad; Tejeddin, Golnaz; Khatibi, Toktam – Education and Information Technologies, 2023
Increasing productivity in educational systems is of great importance. Researchers are keen to predict the academic performance of students; this is done to enhance the overall productivity of educational system by effectively identifying students whose performance is below average. This universal concern has been combined with data science…
Descriptors: Algorithms, Grade Point Average, Interdisciplinary Approach, Prediction
Peer reviewed Peer reviewed
Direct linkDirect link
Robert L. Peach; Sophia N. Yaliraki; David Lefevre; Mauricio Barahona – npj Science of Learning, 2019
The widespread adoption of online courses opens opportunities for analysing learner behaviour and optimising web-based learning adapted to observed usage. Here, we introduce a mathematical framework for the analysis of time-series of online learner engagement, which allows the identification of clusters of learners with similar online temporal…
Descriptors: Learning Analytics, Web Based Instruction, Online Courses, Learner Engagement
Misanchuk, Earl R. – Journal of Instructional Development, 1985
This personal reaction to Cummings'"Comparison of Three Algorithms for Analyzing Questionnaire-Type Needs Assessment Data to Establish Need Priorities" specifically questions Cummings' use of the Mean Difference Analysis for comparison with Weighted Needs Index, and his focus on characteristics of the statistic that are of secondary…
Descriptors: Algorithms, Communication (Thought Transfer), Comparative Analysis, Data Analysis
Peer reviewed Peer reviewed
Dreger, Ralph Mason; And Others – Multivariate Behavioral Research, 1988
Seven data sets (namely, clinical data on children) were subjected to clustering by seven algorithms--the B-coefficient, Linear Typal Analysis; elementary linkage analysis, Numerical Taxonomy System, Statistical Analysis System hierarchical clustering method, Taxonomy, and Bolz's Type Analysis. The little-known B-coefficient method compared…
Descriptors: Algorithms, Children, Clinical Diagnosis, Cluster Analysis
Cummings, Oliver W. – Journal of Instructional Development, 1985
This study of training needs utilized a questionnaire to assess participant's opinions of knowledge level that should exist and knowledge level that does exist in 25 content areas and analyzed the resultant data using three different approaches: mean difference analysis, multicomponent data analysis, and weighted need index. (MBR)
Descriptors: Algorithms, Attitude Measures, Comparative Analysis, Data Analysis