RT Journal Article
T1 Analysis of the Factors Influencing Learners' Performance Prediction With Learning Analytics
A1 Moreno-Marcos, Pedro Manuel
A1 Muñoz Merino, Pedro José
A1 Delgado Kloos, Carlos
AB The advancement of learning analytics has enabled the development of predictive models to forecast learners' behaviors and outcomes (e.g., performance). However, many of these models are only applicable to specific learning environments and it is usually difficult to know which factors influence prediction results, including the predictor variables as well as the type of prediction outcome. Knowing these factors would be relevant to generalize to other contexts, compare approaches, improve the predictive models and enhance the possible interventions. In this direction, this work aims to analyze how several factors can make an influence on the prediction of students' performance. These factors include the effect of previous grades, forum variables, variables related to exercises, clickstream data, course duration, type of assignments, data collection procedure, question format in an exam, and the prediction outcome (considering intermediate assignment grades, including the final exam, and the final grade). Results show that variables related to exercises are the best predictors, unlike variables about forum, which are useless. Clickstream data can be acceptable predictors when exercises are not available, but they do not add prediction power if variables related to exercises are present. Predictive power was also better for concept-oriented assignments and best models usually contained only the last interactions. In addition, results showed that multiple-choice questions were easier to predict than coding questions, and the final exam grade (actual knowledge at a specific moment) was harder to predict than the final grade (average knowledge in the long term), based on different assignments during the course.
SN 2169-3536
YR 2020
FD 2020-01-01
LK https://hdl.handle.net/10016/31048
UL https://hdl.handle.net/10016/31048
LA eng
NO This work was supported in part by the FEDER/Ministerio de Ciencia, Innovación y Universidades–Agencia Estatal de Investigación,through the Smartlet Project under Grant TIN2017-85179-C3-1-R, in part by the Madrid Regional Government through the e-Madrid-CMProject under Grant S2018/TCS-4307, a project which is co-funded by the European Structural Funds (FSE and FEDER), in part by theMinisterio de Ciencia, Innovación y Universidades under Grant FPU016/00526 and Grant EST18/00554, in part by the Hong Kong RGC’sTheme-Based Research Scheme under Grant T44-707/16-N, and in part by the Innovation and Technology Fund under Grant ITS/388/17FP.
DS e-Archivo
RD 17 jul. 2024