scholarly journals Framework for Personalised Online Education based on Learning Analytics through the use of Domain-Specific Modelling and Data Analytics

2019 ◽  
Author(s):  
Sofia Meacham ◽  
Detlef Nauck ◽  
Han Zhao
Keyword(s):  
Online Education ◽  
Learning Analytics ◽  
Data Analytics ◽  
Domain Specific

scholarly journals Using Learning Analytics to Assess Student Learning in Online Courses

2016 ◽  
Vol 13 (3) ◽  
pp. 110-130 ◽  
Author(s):  
Florence Martin ◽  
◽  
Abdou Ndoye ◽  
Keyword(s):  
Learning Analytics ◽  
Data Analytics ◽  
Qualitative Data ◽  
Online Courses ◽  
Instructional Designers ◽  
Data Sets ◽  
Online Assessments ◽  
Visualization Tools ◽  
And Performance ◽  
Analyze Data

Learning analytics can be used to enhance student engagement and performance in online courses. Using learning analytics, instructors can collect and analyze data about students and improve the design and delivery of instruction to make it more meaningful for them. In this paper, the authors review different categories of online assessments and identify data sets that can be collected and analyzed for each of them. Two different data analytics and visualization tools were used: Tableau for quantitative data and Many Eyes for qualitative data. This paper has implications for instructors, instructional designers, administrators, and educational researchers who use online assessments.

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.

Learning Analytics and Technology Through Teaching

2021 ◽  
Author(s):  
Matthew Kaufman ◽  
Kristi Yuthas
Keyword(s):  
Learning Analytics ◽  
Data Analytics ◽  
New Technologies ◽  
Teaching Approach ◽  
Knowledge Gap ◽  
Technical Knowledge ◽  
Self Regulated Learning ◽  
Logical Sequence ◽  
Regulated Learning ◽  
Pedagogical Framework

Data analytics problems, methods and software are changing rapidly. Learning how to learn new technologies might be the most important skill for students to develop in an analytics course. We present a pedagogical framework that promotes self-regulated learning and metacognition and three student-driven assignments that can be used in accounting analytics and other courses that incorporate technology. The assignment can be used by faculty who do not have training in analytics. The assignments adopt a learn-through-teaching approach that helps students: 1) define a conceptual or technical knowledge gap; 2) identify resources available for filling that gap; 3) work independently to acquire the desired knowledge; 4) break knowledge into components and arrange in a logical sequence; and 5) reinforce knowledge by presenting to others in an accessible manner. These assignments equip students with confidence and capabilities that will enable them to keep up with advances in technology.

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.

scholarly journals Designing Pedagogic Conversational Agents through Data Analysis

TecnoLógicas ◽  
2020 ◽  
Vol 23 (47) ◽  
pp. 243-256
Author(s):  
Diana Pérez-Marín ◽  
Silvia Tamayo-Moreno
Keyword(s):  
Learning Process ◽  
Learning Analytics ◽  
Data Analytics ◽  
Teaching And Learning ◽  
Knowledge Discovery In Databases ◽  
Learning Design ◽  
Conversational Agents ◽  
Knowledge Domains ◽  
Use Of Data ◽  
Teaching Learning

Pedagogical Conversational Agents are systems or programs that represent a resource and a means of learning for students, making the teaching and learning process more enjoyable. The aim is to improve the teaching-learning process. Currently, there are many agents being implemented in multiple knowledge domains. In our previous work, a methodology for designing agents was published, the result of which was Agent Dr. Roland, the first conversational agent for Early Childhood Education. In this paper, we propose the use of Data Analytics techniques to improve the design of the agent. Two new techniques are applied: KDDIAE, application of (Knowledge Discovery in Databases) to the Data of the Interaction between Agents and Students – Estudiantes in Spanish, and BIDAE (use of Data Analytics to obtain information of agents and students). The use of KDDIAE and BIDAE proves the existence of a fruitful relationship between learning analytics and learning design. Some samples of rules related to learning analytics and design are the following: (Learning Analytics) Children who initially do not know how to solve the exercise, after receiving help, are able to understand  and solve it à (Learning Design) An agent for small children should be able to provide help. In addition, help should be entertaining and tailored to their characteristics because it is a resource that children actually use; or (Learning Analytics) Younger children use more voice interaction à (Learning Design) An agent interface for young children must incorporate voice commands. A complete list of rules related to learning analytics and design is provided for any researcher interested in PCA design. 72 children were able to use the new Dr. Roland after applying the learning analytics-design rules. They reported a 100 % satisfaction as they all enjoyed interacting with the agent.

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