Do Optional Activities Matter in Virtual Learning Environments?

Abstract

Abstract. Virtual Learning Environments (VLEs) provide students with activities to improve their learning (e.g., reading texts, watching videos or solving exercises). But VLEs usually also provide optional activities (e.g., changing an avatar profile or setting goals). Some of these have a connection with the learning process, but are not directly devoted to learning concepts (e.g., setting goals). Few works have dealt with the use of optional activities and the relationships between these activities and other metrics in VLEs. This paper analyzes the use of optional activities at different levels in a specific case study with 291 students from three courses (physics, chemistry and mathematics) using the Khan Academy platform. The level of use of the different types of optional activities is analyzed and compared to that of learning activities. In addition, the relationship between the usage of optional activities and different student behaviors and learning metrics is presented.

Improving the prediction of learning outcomes in educational platforms including higher level interaction indicators

“…As future work, we would like to extend this model with even more behavioural parameters, such as the interest and behaviour of students with gamification elements (Ruipérez-Valiente, Muñoz-Merino, & Delgado Kloos, (2017)). Currently, we are using only variables related to their interaction with the contents in the platform, but it would also be promising to follow a more mixed methods approach, for example, by combining other information such as demographics and survey data into the model.…”

“…Some of these variables try to improve the ones that were used previously and others are completely new and related to behaviours of students while interacting with the platform which represent complex indicators. We present next the new set of variables that have been taken into consideration for the new prediction model: optional_activities : this variable measures the number of optional activities (such as setting up an avatar or learning goals) that have been used by the student (Ruipérez‐Valiente, Muñoz‐Merino, Delgado Kloos, Niemann, & Scheffel, ). correct_exercises_no_help : percentage of exercises correctly solved by the student of the total number that the student attempted to solve, without the use of hints and in their first attempt (it is possible to attempt several times if the student fails to answer correctly). exercise_effectiveness : this is a specific variable with uses a non‐linear function to measure the total progress of students in exercises, taking into account that most of the exercises were parametric (Muñoz‐Merino, Ruipérez‐Valiente, Alario‐Hoyos, Pérez‐Sanagustín, & Delgado‐Kloos, ). video_effectiveness : this is a specific variable which also uses a non‐linear function to measure the total progress of students in videos, taking into account the specificities of the videos developed for these courses (Muñoz‐Merino et al ). mean_daytime : this measure represents the mean of the time spent each day. It takes into account the data from the pretest date to the post‐test date. variance_daytime : this measure represents the variance of the time spent each day.…”

Evaluation of a learning analytics application for open edX platform

“…Once these parameters have been implemented, we have to make use of the new information related to the learning process. We can find many studies that apply data mining techniques in order to obtain conclusions, for example, relationship mining to see what is the relationship of optional activities and learning indicators in Khan Academy courses [15] and predictive analysis with the objective of preventing students from dropping out in Coursera courses [16]. Another possible outcome is the development of recommendation systems, for example, to recommend specific papers that are more adequate for a learner's goal [17].…”