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Sharma, Sunny; Rana, Vijay; Malhotra, Manisha – Education and Information Technologies, 2022
Web recommendation systems are ubiquitous in the world used to overcome the product overload on e-commerce websites. Among various filtering algorithms, Collaborative Filtering and Content Based Filtering are the best recommendation approaches. Being popular, these filtering approaches still suffer from various limitations such as Cold Start…
Descriptors: Web Sites, Purchasing, Computer Software, Profiles
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
Göktepe Körpeoglu, Seda; Göktepe Yildiz, Sevda – Education and Information Technologies, 2023
Examining students' attitudes towards STEM (science, technology, engineering, and mathematics) fields starting from middle school level is important in their career choices and future planning. However, there is a need to investigate which variables affect students' attitudes towards STEM. Here, we aimed to estimate middle school students'…
Descriptors: Comparative Analysis, Algorithms, Data Collection, Student Attitudes
Bakhshinategh, Behdad; Zaiane, Osmar R.; ElAtia, Samira; Ipperciel, Donald – Education and Information Technologies, 2018
Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning…
Descriptors: Educational Environment, Surveys, Comparative Analysis, Data Analysis
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
Nahar, Khaledun; Shova, Boishakhe Islam; Ria, Tahmina; Rashid, Humayara Binte; Islam, A. H. M. Saiful – Education and Information Technologies, 2021
Information is everywhere in a hidden and scattered way. It becomes useful when we apply Data mining to extracts the hidden, meaningful, and potentially useful patterns from these vast data resources. Educational data mining ensures a quality education by analyzing educational data based on various aspects. In this paper, we have analyzed the…
Descriptors: Learning Analytics, College Students, Engineering Education, Data Collection
Friedman, Alon – Education and Information Technologies, 2019
Learning analytics is an emerging field in which educators and researchers are using data to improve their students' educational experiences. One of the most common courses offered by higher academic institutions in the US is data science. This paper examines the data science syllabi found in today's academic sector and compares the results to…
Descriptors: Course Descriptions, Comparative Analysis, Data Analysis, Educational Experience
Lansigan, Rolando R.; Moraga, Shirley D.; Batalla, Ma. Ymelda C.; Bringula, Rex P. – Education and Information Technologies, 2016
This descriptive study utilized a validated questionnaire that gathered data from freshmen of two different school years. Demographic profile, marketers (i.e., source of information of students about the school), influencers (i.e., significant others that persuaded them to enroll in the school), level of school choice, and level of consideration…
Descriptors: Foreign Countries, College Choice, Social Media, College Freshmen
Taçgin, Zeynep; Arslan, Ahmet – Education and Information Technologies, 2017
The purpose of this study is to determine perception of postgraduate Computer Education and Instructional Technologies (CEIT) students regarding the concepts of Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), Augmented Virtuality (AV) and Mirror Reality; and to offer a table that includes differences and similarities between…
Descriptors: Graduate Students, Student Attitudes, Computer Science Education, Educational Technology