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 Data Preparation in Predictive Learning Analytics (PLA) for Student Dropout

2020 ◽  
Vol 9 (3S) ◽  
pp. 116-120
Keyword(s):  
Learning Analytics ◽  
Policy Making ◽  
Data Cleaning ◽  
Current Trend ◽  
Educational Institutions ◽  
Future Research ◽  
Preparation Process ◽  
Data Preparation ◽  
Student Dropout ◽  
Predictive Learning

Predictive learning analytics (PLA) are the current trend to support learning processes. One of the main issues in education particularly in higher education (HE) is high numbers of dropout. There are little evidences being identified the variables contributing toward dropout during study period. The dropout are the major challenges of educational institutions as it concerns in the education cost and policy-making communities. The paper presents a data preparation process for student dropout in Duta Bangsa University. The number of students dropout in Duta Bangsa University are in high alarm for both management and also educator in Duta Bangsa. Preventing educational dropout are the major challenges to Duta Bangsa University. Data preparation is an important step in PLA processes, the main objective is to reduce noise and increase the accuracy and consistency of data before PLA executed. The data preparation on this paper consist of four steps: (1) Data Cleaning, (2) Data Integration, (3) Data Reduction, and (4) Data Transformation. The results of this study are accurate and consistent historical dropout data Duta Bangsa University. Furthermore, this paper highlights open challenges for future research in the area of PLA student dropout

scholarly journals Early Dropout Prediction in MOOCs through Supervised Learning and Hyperparameter Optimization

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1701
Author(s):  
Theodor Panagiotakopoulos ◽  
Sotiris Kotsiantis ◽  
Georgios Kostopoulos ◽  
Omiros Iatrellis ◽  
Achilles Kameas
Keyword(s):  
Online Education ◽  
Online Courses ◽  
Early Stage ◽  
Activity Patterns ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Educational Institutions ◽  
Student Dropout ◽  
Wide Range

Over recent years, massive open online courses (MOOCs) have gained increasing popularity in the field of online education. Students with different needs and learning specificities are able to attend a wide range of specialized online courses offered by universities and educational institutions. As a result, large amounts of data regarding students’ demographic characteristics, activity patterns, and learning performances are generated and stored in institutional repositories on a daily basis. Unfortunately, a key issue in MOOCs is low completion rates, which directly affect student success. Therefore, it is of utmost importance for educational institutions and faculty members to find more effective practices and reduce non-completer ratios. In this context, the main purpose of the present study is to employ a plethora of state-of-the-art supervised machine learning algorithms for predicting student dropout in a MOOC for smart city professionals at an early stage. The experimental results show that accuracy exceeds 96% based on data collected during the first week of the course, thus enabling effective intervention strategies and support actions.

scholarly journals Demystifying Learning Analytics in Personalised Learning

2018 ◽  
Vol 7 (3) ◽  
pp. 1124 ◽  
Author(s):  
Andino Maseleno ◽  
Noraisikin Sabani ◽  
Miftachul Huda ◽  
Roslee Ahmad ◽  
Kamarul Azmi Jasmi ◽  
...  
Keyword(s):  
Learning Environment ◽  
Learning Process ◽  
Learning Analytics ◽  
Educational Institutions ◽  
Learning Resources ◽  
Personalised Learning ◽  
Report Data ◽  
Overall Performance ◽  
The Relationship ◽  
Specific Learning

This paper presents learning analytics as a mean to improve students’ learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper’s contribution to knowledge is in considering personalised learning within the context framework of learning analytics. 

scholarly journals STUDENTS PERCEPTION’S TOWARDS ONLINE TEACHING: AN EMPIRICAL STUDY IN BERHAMPUR UNIVERSITY

2020 ◽  
pp. 196-213
Author(s):  
Bharat Kumar Lakra
Keyword(s):  
Regression Analysis ◽  
Online Education ◽  
Student Perceptions ◽  
Online Teaching ◽  
Teaching And Learning ◽  
Student Perception ◽  
Perceived Usefulness ◽  
Educational Institutions ◽  
Online Class ◽  
Teaching Characteristics

In the Coronavirus disease -19 (COVID-19) response, all the educational institutions have been compulsory to move all teaching and learning activities online at very short time notice by the University Grand Commission (UGC). Consequently, all classes, simulations, practicums, viva-voces, and valuation, etc., were adapted for the online setting. Online education has been at the forefront of discussions as a new and viable option for learning opportunities in higher education. Academic institutions continue to see remarkable growth of online education during COVID-19. Due to the pandemic situation, UGC has instruction with 40 per cent of online learning. Hence educational institutions have been implementing online classes. The article investigates to identify the factors which students perceive significant influence towards online class. From a student perception, there has been adaptation and the prospect to advance new skills, possibly providing online teaching via elearning or virtual learning. Further, this study sought to provide an investigation of online teaching in University with an intention on how the teaching and learning interaction will affect students ‘perceptions relating to their online class preparedness and experiences. The results revealed from descriptive statistics, correlation, and regression analysis that students reported a moderate relationship between the extracted factor scores and overall satisfaction of online teaching. The findings of four factors that affect the students' views about online teaching characteristics instruction seen that student perceptions about online teaching are positively affected by "Perceived Usefulness” of online teaching. The second most important factor is student supportiveness, followed by faculty responsiveness and perceived flexibility. Further, Multiple Regression Analysis has been analysed to inspect the relationship between the various online teaching characteristics and the overall satisfaction from online teaching. Thus, this study may be helpful to teachers in constructing proper pedagogical techniques which can be suitable and beneficial for learning, understanding and application of the online teaching-learning process.

scholarly journals Challenges of the adult learning sector in context of COVID-19 in Latvia

2021 ◽  
Author(s):  
Inga Jekabsone ◽  
◽  
Ina Gudele ◽  
Keyword(s):  
Adult Learners ◽  
Adult Learning ◽  
Online Education ◽  
Regional Development ◽  
Good Practice ◽  
Emergency Situation ◽  
Educational Institutions ◽  
Ministry Of Education ◽  
Online Tools ◽  
Remote Learning

The COVID-19 pandemic has affected the way people work and learn in unprecedented ways. Also, the pandemic has moved more business activity online, increasing the need for training and prompting them to build more online trainings. In this time of crisis, a suitable response requires novel ways to enable interaction between adult learners, adult learners and teachers, adult learners and content using online tools so that no one is left behind. In the context of regional development, online adult learning provides economic active inhabitants with wide opportunities since employees are able to attend high-quality trainings regardless the place of residence. In context of COVID-19, during the emergency situation Latvia has fully moved to remote learning, including adult learning. Educational institutions as well as enterprises that organise trainings for adults have to implement remote learning using several online tools. The aim of the paper is to analyse the main challenges of the adult learning sector in Latvia in context of COVID-19 taking into consideration the regional development issues. In order to achieve the aim, following research methods have been used: scientific literature studies, statistical data analysis, interviews. Main results of the survey: in case of Latvia, the Ministry of Education and Science of Republic of Latvia has launched several initiatives towards enabling the shift to online learning, providing recommendations, digital tools as well as good practice sharing. At the same time, there is no methodology and detailed step-by-step recommendations, how to develop the online education learning for educational institutions in Latvia. However, there are incentives to develop online adult learning via project funding.

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.

MOOCs Business Models

2020 ◽  
pp. 1189-1214
Author(s):  
Amir Manzoor
Keyword(s):  
Higher Education ◽  
Online Education ◽  
Rapid Growth ◽  
Return On Investment ◽  
Business Models ◽  
Focal Point ◽  
Faculty Members ◽  
Viral Nature

MOOCs have grabbed the headlines and rightfully become the focal point of the disruption under way in higher education. The environment in which MOOCs and other forms of online education operate is changing virtually every day. The viral nature of MOOCs has been apparent through the rapid growth of providers, participating (significant) institutions, faculty members involved in providing courses, students enrolled, and other measures. And MOOCs are starting to exhibit the second trend desired by their startup investors: MOOCs don't seem to be going away. More courses are being added, more faculty members and students are becoming involved. While MOOCs have captured the interest of many, the business models and return on investment are still evolving. The aim of this chapter is to present an analysis of various business models being used by various MOOCs providers along with some future monetization strategies for MOOC providers.

COVID-19

2022 ◽  
pp. 219-227
Author(s):  
Gillala Rekha
Keyword(s):  
Mental Health ◽  
Online Education ◽  
Private University ◽  
Well Being ◽  
Academic Stress ◽  
Educational Institutions ◽  
The World ◽  
And Coping ◽  
Quantitative And Qualitative Methods ◽  
Health And Well Being

Due to the COVID-19 pandemic, governments around the world closed all the educational institutions to control the spread of disease, which is creating a direct impact on students, educators, and institutions. The purpose of this study was to analyze the perception of academic stress experienced by students during current online education and coping strategies using emotional intelligence adopted by them. The study aims to conduct a timely assessment of the effects of stress due to COVID-19 pandemic on the mental health of college students. The authors conducted interview surveys with 227 students at a private university in India to understand the effects of online education during pandemic on their mental health and well-being. The data were analyzed through quantitative and qualitative methods. Of the 227 students, 71% indicated anxiety and stress due to ongoing pandemic.

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