scholarly journals Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning

2020 ◽  
Vol 10 (5) ◽  
pp. 1722 ◽  
Author(s):  
Pedro Manuel Moreno-Marcos ◽  
Pedro J. Muñoz-Merino ◽  
Carlos Alario-Hoyos ◽  
Carlos Delgado Kloos
Keyword(s):  
Learning Environments ◽  
Predictive Models ◽  
Student Persistence ◽  
Online Courses ◽  
Strong Predictor ◽  
Student Behaviors ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
And Performance ◽  
Reported Data

In education, several studies have tried to track student persistence (i.e., students’ ability to keep on working on the assigned tasks) using different definitions and self-reported data. However, self-reported metrics may be limited, and currently, online courses allow collecting many low-level events to analyze student behaviors based on logs and using learning analytics. These analyses can be used to provide personalized and adaptative feedback in Smart Learning Environments. In this line, this work proposes the analysis and measurement of two types of persistence based on students’ interactions in online courses: (1) local persistence (based on the attempts used to solve an exercise when the student answers it incorrectly), and (2) global persistence (based on overall course activity/completion). Results show that there are different students’ profiles based on local persistence, although medium local persistence stands out. Moreover, local persistence is highly affected by course context and it can vary throughout the course. Furthermore, local persistence does not necessarily relate to global persistence or engagement with videos, although it is related to students’ average grade. Finally, predictive analysis shows that local persistence is not a strong predictor of global persistence and performance, although it can add some value to the predictive models.

scholarly journals Exploring Factors and Indicators for Measuring Students’ Performance in Moodle Learning Environment

2021 ◽  
Vol 16 (12) ◽  
pp. 169
Author(s):  
Iman Rashid Al-Kindi ◽  
Zuhoor Al-Khanjari
Keyword(s):  
Learning Strategies ◽  
Learning Process ◽  
Online Courses ◽  
Smart Cities ◽  
Sultan Qaboos University ◽  
Personal Learning ◽  
Smart Learning ◽  
Holistic Understanding ◽  
And Performance ◽  
Data Log

One of the most important pillars of smart cities is the smart learning environ-ment. This environment should be well prepared and managed to improve the in-struction process for instructors from one side and the learning process for stu-dents from the other side. This paper presents the student’s Engagement, Behav-ior and Personality (EBP) predictive model. This model uses Moodle log data to investigate the influence and the effect of the students’ EBP factors on their per-formance. For this purpose, this paper uses the data log files of the "Search Strat-egies on the Internet" online course in Fall 2019 at Sultan Qaboos University (SQU) extracted from Moodle database. The intention of conducting this kind of experiments is of three-facets: 1. to assist in gaining a holistic understanding of online learning environments by focusing on student EBP and performance with-in the course activities, 2. to explore whether the student’s EBP can be considered as indicators for predicting student’s performance in online courses, and 3. to support instructors with insights to develop better learning strategies and tailor instructions for personal learning of individual students. Moreover, this paper takes a step forward in identifying effective methods to measure student’s EBP during the learning process. This may contribute to proposing a framework for the smart learning behavior environment that would guide the instructors to ob-serve students’ performance in a more creative way. All the 38 students who participated in this experiment had compatible statistics and results as the relationship between their Engagement, Behavior, Personality was symmetric with their Performance. This relationship was presented using a group of condition rules (If-then). The extracted rules gave us a straightforward and visual picture of the rela-tionship between the factors mentioned in this paper.

Open Learner Models in Smart Learning Environments

2019 ◽  
pp. 346-368 ◽  
Author(s):  
Angeliki Leonardou ◽  
Maria Rigou ◽  
John D. Garofalakis
Keyword(s):  
Learning Environments ◽  
Adaptive Learning ◽  
Real Life ◽  
Self Reflection ◽  
Learner Model ◽  
Self Regulated Learning ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
Navigation Support ◽  
Adaptive Learning Systems

Smart learning environments (SLEs), like all adaptive learning systems, are built around the learner model and use it to support a variety of interventions such as mastery learning, scaffolding, adaptive sequencing, and adaptive navigation support. Open learner models (OLMs) “expose” the learner data to users through easily perceivable visual representations aiming to improve student self-reflection and self-regulated learning and also increase user motivation and even foster collaboration. This chapter presents the evolution and current state of OLMs, summarizes related research in the field emphasizing on OLM types, locus of control between the system and the user and visualizations categorized on the basis of quantized/continuous and structured/unstructured representations. OLM cases implementing typical SLEs features are described, along with representative real-life scenarios of incorporating OLMs in SLEs. Moreover, the chapter provides guidelines for designing effective OLMs and discusses current research trends in this active scientific field.

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