WELCOME FROM THE STREAM EDITOR: LEARNING ANALYTICS IN ONLINE EDUCATION

2017 ◽  
Vol 1 (1) ◽  
pp. 3
Author(s):  
Ananda Gunawardena
Keyword(s):  
Online Education ◽  
Learning Analytics

scholarly journals Responsive Dashboard as a Component of Learning Analytics System for Evaluation in Emergency Remote Teaching Situations

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7998
Author(s):  
Emilia Corina Corbu ◽  
Eduard Edelhauser
Keyword(s):  
Online Education ◽  
Learning Analytics ◽  
Evaluation System ◽  
Online Courses ◽  
Evaluation Model ◽  
Evaluation Process ◽  
Educational Process ◽  
Study Objective ◽  
Teachers And Students ◽  
The Evaluation System

The pandemic crisis has forced the development of teaching and evaluation activities exclusively online. In this context, the emergency remote teaching (ERT) process, which raised a multitude of problems for institutions, teachers, and students, led the authors to consider it important to design a model for evaluating teaching and evaluation processes. The study objective presented in this paper was to develop a model for the evaluation system called the learning analytics and evaluation model (LAEM). We also validated a software instrument we designed called the EvalMathI system, which is to be used in the evaluation system and was developed and tested during the pandemic. The optimization of the evaluation process was accomplished by including and integrating the dashboard model in a responsive panel. With the dashboard from EvalMathI, six online courses were monitored in the 2019/2020 and 2020/2021 academic years, and for each of the six monitored courses, the evaluation of the curricula was performed through the analyzed parameters by highlighting the percentage achieved by each course on various components, such as content, adaptability, skills, and involvement. In addition, after collecting the data through interview guides, the authors were able to determine the extent to which online education during the COVID 19 pandemic has influenced the educational process. Through the developed model, the authors also found software tools to solve some of the problems raised by teaching and evaluation in the ERT environment.

A Complete Validated Learning Analytics Framework

2018 ◽  
Vol 14 (2) ◽  
pp. 1-16 ◽  
Author(s):  
Ahmed Tlili ◽  
Fathi Essalmi ◽  
Mohamed Jemni ◽  
Kinshuk ◽  
Nian-Shing Chen
Keyword(s):  
Online Education ◽  
Rapid Growth ◽  
Learning Analytics ◽  
Learning Experiences ◽  
Educational Institutions ◽  
Data Preparation ◽  
Overall Effectiveness

With the rapid growth of online education in recent years, Learning Analytics (LA) has gained increasing attention from researchers and educational institutions as an area which can improve the overall effectiveness of learning experiences. However, the lack of guidelines on what should be taken into consideration during application of LA hinders its full adoption. Therefore, this article investigates the issues that should be considered when approaching the design of LA experiences from the data preparation perspective. The obtained results highlight a validated LA framework of twenty-two designing issues that should be considered by various stakeholders in different contexts as well as a set of guidelines which can enhance designing LA experiences.

scholarly journals Extraction of Student Interaction Data from an Open edX Platform

2021 ◽  
Vol 20 (38) ◽  
pp. 87-98
Author(s):  
Daniel Jaramillo-Morillo ◽  
Mario Solarte ◽  
Gustavo Ramírez-González
Keyword(s):  
Online Education ◽  
Learning Analytics ◽  
Online Courses ◽  
Student Interaction ◽  
Massive Open Online Courses ◽  
Interaction Data ◽  
Access To Education ◽  
Huge Number ◽  
Massive Open Online ◽  
Java Code

The Massive Open Online Courses (MOOC) are courses available to the general public without restrictions that are offered to hundreds or thousands of students and in recent years have been presented as a revolution in online education. They are presented as an alternative to the great demand in higher education for the characteristic of being open and massive because they allow access to education to a huge number of students. They have become an ideal environment for data collection and through the application of learning analytics techniques they have allowed a better understanding of how students learn. However, access to the data from thecurrent open-source MOOC platforms is limited and often difficult to collect and process. This paper presents a proposal for collecting and processing the data from students’ interaction with the Open edX platform through Scripts and a Collector based on Java code. 

scholarly journals Including Learning Analytics in the Loop of Self-Paced Online Course Learning Design

2020 ◽  
Author(s):  
Hongxin Yan ◽  
Fuhua Lin ◽  
Kinshuk
Keyword(s):  
Online Education ◽  
Learning Analytics ◽  
Course Design ◽  
Online Course ◽  
Full Potential ◽  
Learning Design ◽  
Design Strategies ◽  
Student Modelling ◽  
Tutoring Systems ◽  
Online Educators

AbstractOnline education is growing because of its benefits and advantages that students enjoy. Educational technologies (e.g., learning analytics, student modelling, and intelligent tutoring systems) bring great potential to online education. Many online courses, particularly in self-paced online learning (SPOL), face some inherent barriers such as learning awareness and academic intervention. These barriers can affect the academic performance of online learners. Recently, learning analytics has been shown to have great potential in removing these barriers. However, it is challenging to achieve the full potential of learning analytics with the traditional online course learning design model. Thus, focusing on SPOL, this study proposes that learning analytics should be included in the course learning design loop to ensure data collection and pedagogical connection. We propose a novel learning design-analytics model in which course learning design and learning analytics can support each other to increase learning success. Based on the proposed model, a set of online course design strategies are recommended for online educators who wish to use learning analytics to mitigate the learning barriers in SPOL. These strategies and technologies are inspired by Jim Greer’s work on student modelling. By following these recommended design strategies, a computer science course is used as an example to show our initial practices of including learning analytics in the course learning design loop. Finally, future work on how to develop and evaluate learning analytics enabled learning systems is outlined.

Designing Online Learning Strategies through Analytics

2014 ◽  
pp. 1-15 ◽  
Author(s):  
Prerna Lal
Keyword(s):  
Student Engagement ◽  
Online Education ◽  
Learning Strategies ◽  
Learning Process ◽  
Learning Analytics ◽  
Online Data ◽  
Institutional Planning ◽  
Education Environment ◽  
Day By Day ◽  
Video Format

The online education environment is becoming complex day-by-day. Nowadays, educational institutes are offering various types of courses online to a large number of students having a diverse background, with the flexibility of time and geography. This results in creating a large repository of online data regarding courses, students and instructors. These data may be in text, audio or video format. This chapter is an attempt to understand the use of Learning Analytics that advocates for analysis of these data and to understand the learning process better in terms of student engagement, pedagogy, content and assessment. Educational institutes can utilize the intelligence revealed by learning analytics processes, and communicate them to those involved in strategic institutional planning.

scholarly journals Educational analytics. A new approach to improving the quality of the educational process

2020 ◽  
Author(s):  
D. Oskin ◽  
◽  
A. Oskin ◽  
Keyword(s):  
Online Education ◽  
Programming Language ◽  
Learning Analytics ◽  
Educational Process ◽  
Classification Model ◽  
New Approach ◽  
High Level ◽  
Student Classification

This article describes the trends in online education caused by the COVID-19 pandemic. The introduction of learning analytics into the educational process is substantiated. The main methods and tools of educational analytics are considered. Using a specific example, we will understand the construction and assessment of a student classification model using the high-level programming language Python.

scholarly journals Correlation between Grades Earned and Time in Online Courses

2017 ◽  
Vol 21 (4) ◽  
Author(s):  
Lin Carver ◽  
Keya Muhkerjee ◽  
Robert Lucio
Keyword(s):  
Online Education ◽  
Graduate Education ◽  
Student Performance ◽  
Learning Analytics ◽  
Online Courses ◽  
Course Delivery ◽  
Education Students ◽  
Insight Into ◽  
Specific Letter ◽  
Synchronous Online

Online education is rapidly becoming a significant method of course delivery in higher education. Consequently instructors are analyzing student performance in an attempt to better scaffold student learning. Learning analytics can provide insight into online students’ course behaviors. Archival data from 167 graduate level education students enrolled in 4 different programs and 9 different online courses was analyzed in an attempt to determine if there was a correlation between their grades and the time spent in specific areas within the course: the total time within the course, the course modules, document repository, and synchronous online sessions. Data was analyzed by total time in course, time in modules, time in document repository, and time in the online synchronous discussions as well as by program. Time spent in each component did not correlate with the specific letter grade, but did correlate with earning an A or not earning an A. The sample was composed of students from four different graduate education programs: Educational Leadership, Reading, Instructional Design, and Special Education. Variations were found between programs, but the differences did not significantly correlate with the grade earned in the course. A logical progression revealed that of all the predictor variables, only time spent in synchronous online sessions showed as a significant predictor of receiving an A in the course. This is important information for instructor when providing scaffolding for students.

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