Identifying the impact of friends on their peers academic performance

Abstract

Abstract-Historically data collection in the research process involves either surveys, interviews or observation, or any combination of all three. Recent developments in the area of formative educational methods have enabled other data collection options. Data sources now available include university Virtual Learning Environments (VLEs), E-learning and many other knowledge management systems. Data-sets harvested from these sources are less susceptible to the inherent biases introduced through the intervention of human interpretation. Data is often structured, complete and traceable. The research in this paper aims to utilise one of these unique digital data-sets which represents the footprints created by student activities within a university environment and through Social Network Analysis to identify their influences within peer groups.

The Synthesis of the Student Relationship Management System Using the Internet of Things to Collect the Digital Footprint for Higher Education Institutions

“…• Demographic data • Academic records • Assignments and test results • Library access and borrow & return history and 5) access to WiFi or other networks [31].…”

Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data

“…By analyzing the effect of the factors influencing academic performance, many systems using data to predict academic performance have been developed in the literature [1][2][3][4]7,8,[13][14][15][16][17][18][19][22][23][24][25][26][27][29][30][31]33,34,[37][38][39][40][41]. For instance, in [8], academic performance was predicted based on passive sensing data and self-reports from students' smart phones.…”