scholarly journals Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses

2021 ◽  
Vol 11 (21) ◽  
pp. 10145
Author(s):  
Aurora Esteban ◽  
Cristóbal Romero ◽  
Amelia Zafra
Keyword(s):  
Distance Learning ◽  
Student Performance ◽  
Online Courses ◽  
Data Representation ◽  
Multiple Instance Learning ◽  
Machine Learning Algorithms ◽  
Influential Factor ◽  
Student Interactions ◽  
Single Instance ◽  
Public Datasets

Studies on the prediction of student success in distance learning have explored mainly demographics factors and student interactions with the virtual learning environments. However, it is remarkable that a very limited number of studies use information about the assignments submitted by students as influential factor to predict their academic achievement. This paper aims to explore the real importance of assignment information for solving students’ performance prediction in distance learning and evaluate the beneficial effect of including this information. We investigate and compare this factor and its potential from two information representation approaches: the traditional representation based on single instances and a more flexible representation based on Multiple Instance Learning (MIL), focus on handle weakly labeled data. A comparative study is carried out using the Open University Learning Analytics dataset, one of the most important public datasets in education provided by one of the greatest online universities of United Kingdom. The study includes a wide set of different types of machine learning algorithms addressed from the two data representation commented, showing that algorithms using only information about assignments with a representation based on MIL can outperform more than 20% the accuracy with respect to a representation based on single instance learning. Thus, it is concluded that applying an appropriate representation that eliminates the sparseness of data allows to show the relevance of a factor, such as the assignments submitted, not widely used to date to predict students’ academic performance. Moreover, a comparison with previous works on the same dataset and problem shows that predictive models based on MIL using only assignments information obtain competitive results compared to previous studies that include other factors to predict students performance.

Knowledge Discovery of Student Sentiments in MOOCs and Their Impact on Course Performance

2014 ◽  
Author(s):  
Conrad S. Tucker ◽  
Bryan Dickens ◽  
Anna Divinsky
Keyword(s):  
Student Performance ◽  
Language Processing ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Massive Open Online ◽  
Course Performance ◽  
Student Interactions ◽  
Computing Device ◽  
Digital World ◽  
Textual Data

The objective of this research is to mine textual data (e.g., online discussion forums) generated by students enrolled in Massive Open Online Courses (MOOCs) in order to quantify students’ sentiment, in relation to their course performance. Massive Open Online Courses (MOOCs) are free to anyone with a computing device and a means of connecting to the internet and serve as a new paradigm for distance based education. While student interactions in traditional based brick and mortar classes are readily observable by students and instructors, quantifying the sentiments expressed by students in MOOCs remains challenging. This is in part due to the quantity of textual data being generated by students enrolled in MOOCs, in addition to a lack of quantitative methodologies that discover latent, previously unknown knowledge pertaining to student interactions and sentiments in the digital world. The authors of this work introduce a data mining driven methodology that employs natural language processing techniques and text mining algorithms to quantify students’ sentiments, based on their textual data provided during course assignment discussions. The researchers of this work aim to help educators understand the factors that may impact student performance, team interactions and overall learning outcomes in digital environments such as MOOCs.

A Qualitative Study of Student Expectations of Online Faculty Engagement

2019 ◽  
pp. 220-236
Author(s):  
Christoper W. Berg ◽  
Melanie Shaw ◽  
Anthony L. Contento ◽  
Scott W. M. Burrus
Keyword(s):  
Student Satisfaction ◽  
Student Performance ◽  
Online Courses ◽  
Faculty Engagement ◽  
Online Faculty ◽  
Student Expectations ◽  
Performance Expectations ◽  
Student Interactions ◽  
Institutional Expectations ◽  
The Relationship

Institutions offering online courses and degrees often develop requirements for faculty-to-student interactions; yet, these requirements may not align student preferences for faculty engagement. This chapter expanded the work on an earlier study by Shaw, Clowes, and Burrus, “A Comparative Typology of Student and Institutional Expectations of Online Faculty.” The current study included a new sampling of 57 students across two institutions focused on their experiences in online courses. Using the original typology as a lens, results were grouped into themes including substantive feedback, timeliness, and course expectations. Recommendations for further study include conducting a quantitative study of the relationship between faculty outcomes and student satisfaction after implementing student performance expectations.

A Qualitative Study of Student Expectations of Online Faculty Engagement

2021 ◽  
pp. 2020-2031
Author(s):  
Christoper W. Berg ◽  
Melanie Shaw ◽  
Anthony L. Contento ◽  
Scott W. M. Burrus
Keyword(s):  
Student Satisfaction ◽  
Student Performance ◽  
Online Courses ◽  
Faculty Engagement ◽  
Online Faculty ◽  
Student Expectations ◽  
Performance Expectations ◽  
Student Interactions ◽  
Institutional Expectations ◽  
The Relationship

Institutions offering online courses and degrees often develop requirements for faculty-to-student interactions; yet, these requirements may not align student preferences for faculty engagement. This chapter expanded the work on an earlier study by Shaw, Clowes, and Burrus, “A Comparative Typology of Student and Institutional Expectations of Online Faculty.” The current study included a new sampling of 57 students across two institutions focused on their experiences in online courses. Using the original typology as a lens, results were grouped into themes including substantive feedback, timeliness, and course expectations. Recommendations for further study include conducting a quantitative study of the relationship between faculty outcomes and student satisfaction after implementing student performance expectations.

Assessment of Individual Student Performance in Online Team Projects

2011 ◽  
Vol 15 (3) ◽  
Author(s):  
Jay Alden
Keyword(s):  
Student Performance ◽  
Group Process ◽  
Online Courses ◽  
Peer Assessment ◽  
Individual Student ◽  
Assessment Practice ◽  
Group Project ◽  
Team Members ◽  
Team Projects

The use of team projects has been shown to be beneficial in higher education. There is also general agreement that team efforts should be assessed and that the grading ought to represent both (1) the quality of the product developed jointly by the team as well as (2) the degree of participation and quality of contribution by each individual student involved in the group process. The latter grading requirement has posed a challenge to faculty so the question addressed in this paper is “How should individual team members in online courses be assessed for the extent and quality of their contributions to the group project?” To answer this question, four common team member evaluation practices were reviewed and compared to seven criteria representing positive attributes of an assessment practice in an online learning environment. Whereas the Peer Assessment practice received the greatest support in the literature in face-to-face courses, this study that considered the perceptions of graduate faculty and students recommended the Faculty Review practice as the default assessment

Distance learning in primary school: opportunities and specifics of implementation

2020 ◽  
Vol 1(16) (2020) ◽  
pp. 91-98
Author(s):  
Oksana Yastrub ◽  
◽  
◽  
Keyword(s):  
Distance Learning ◽  
Primary School ◽  
Online Courses ◽  
School Teacher ◽  
Educational Process ◽  
Primary School Teacher ◽  
School Students ◽  
Online Mode ◽  
Teachers And Students ◽  
Theoretical Test

The problem of introducing distance learning in primary school is actualized by the development of social networks and Internet technologies, which open unlimited horizons for their application in educational activities. In addition, the introduction of quarantine in Ukraine requires primary school teachers to find ways to effectively master the program material. Among such ways is distance learning. The purpose of the study is to substantiate the specifics and possibilities of organizing the educational process in primary school with the use of distance learning. In the process of scientific research methods of analysis, synthesis, generalization and systematization were used. Distance learning in primary school is defined as a form of organization and implementation of the educational process, in which the subjects of learning (teachers and students) in the online mode carry out educational interaction in principle and mainly extraterritorially. In the context of reforming modern Ukrainian education, a number of e-platforms have been created for the organization of distance learning for primary school students. An effective commercial platform for distance learning is the service "My Class", which contains online courses from 1st to 11th grade, which contain lessons that integrate theoretical (test presentation of content) and practical (individual tasks that can be solved independently of each other, a block of tasks that need to be solved sequentially, guidelines) blocks. Result. Emphasis is placed on the requirements to be met by a primary school teacher when organizing distance learning in primary school during the quarantine period and it is suggested to advise parents who will work remotely with junior students in the initial stages of distance learning, gradually transferring activity to children.

Management of e-resources in academic libraries in Ghana: Copyright implications

IFLA Journal ◽  
2021 ◽  
pp. 034003522110271
Author(s):  
Theresa L Adu ◽  
Thomas B van der Walt
Keyword(s):  
Distance Education ◽  
Mixed Methods ◽  
Distance Learning ◽  
Academic Libraries ◽  
Online Courses ◽  
Qualitative Interviews ◽  
Digital Resources ◽  
Mixed Methods Approach ◽  
Sequential Mixed Methods ◽  
Library Staff

This study investigated the copyright issues surrounding the management of e-resources in academic libraries in Ghana. Forty-seven library staff and head librarians from four academic libraries were engaged using questionnaires and qualitative interviews in a sequential mixed-methods approach to generate data for this study. The findings indicate that in all four institutions copyright issues arose with the provision of distance learning, online courses and e-reserves services. All the respondents stated that they or their colleagues had had faculty ask questions on copyright issues. However, the professional librarians indicated that the library was not consulted and the instructors for online courses or distance education programmes did not cooperate with librarians; rather, the department posting the materials made the decisions on copyright regarding the usage of digital resources for distance learning, online courses or e-reserves. This does not augur well for the management of copyright of e-resources in academic libraries in Ghana.

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 CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation

2021 ◽  
Vol 11 (4) ◽  
pp. 1380
Author(s):  
Yingbo Zhou ◽  
Pengcheng Zhao ◽  
Weiqin Tong ◽  
Yongxin Zhu
Keyword(s):  
Distance Learning ◽  
Feature Learning ◽  
Image Synthesis ◽  
Image Feature ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Feature Representations ◽  
Adversarial Network ◽  
Public Datasets

While Generative Adversarial Networks (GANs) have shown promising performance in image generation, they suffer from numerous issues such as mode collapse and training instability. To stabilize GAN training and improve image synthesis quality with diversity, we propose a simple yet effective approach as Contrastive Distance Learning GAN (CDL-GAN) in this paper. Specifically, we add Consistent Contrastive Distance (CoCD) and Characteristic Contrastive Distance (ChCD) into a principled framework to improve GAN performance. The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. We model the framework by employing Siamese Network as a module into GANs without any modification on the backbone. Both qualitative and quantitative experiments conducted on three public datasets demonstrate the effectiveness of our method.

scholarly journals Time Management Skills and Student Performance in Online Courses

2015 ◽  
Author(s):  
Susan Miertschin ◽  
Carole Goodson ◽  
Barbara Stewart
Keyword(s):  
Student Performance ◽  
Time Management ◽  
Online Courses ◽  
Management Skills

scholarly journals Russian Students’ Readiness for Distance Learning: Current Situation and Future Challenges

2021 ◽  
Vol 25 (2) ◽  
pp. 80-97
Author(s):  
V. N. Kiroy ◽  
D. N. Sherbina ◽  
A. A. Chernova ◽  
E. G. Denisova ◽  
D. M. Lazurenko
Keyword(s):  
Higher Education ◽  
Distance Learning ◽  
Digital Literacy ◽  
Online Courses ◽  
Learning Technologies ◽  
Internet Technologies ◽  
Future Challenges ◽  
Distance Learning Technologies ◽  
Relationship Of ◽  
The Relationship

In the context of the COVID pandemic, there has dramatically increased the significance of distance learning technologies. Higher education will most probably increase their usage even after overcoming the coronavirus. This paper aims at assessing Russian university students’ readiness to exercise distance learning technologies. The survey within Rostov-on-Don universities provided data on 428 students’ skills in using Internet technologies when studying. It is shown that in the pre-pandemic period, no more than a quarter of students had the necessary skills to participate in video conferences, and about 16 % of students took online courses autonomously. Only 6,5 % of the respondents could manage both technologies that comprise distance learning. The results obtained on the relationship between academic performance and self-participation in online courses, as well as on the relationship of these indicators with general digital literacy and immersion in social networks, should be taken into account within wide computerization of education during the pandemic.

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