Analyzing students’ online presence in undergraduate courses using Clustering

Nand, Ravneil; Chand, Ashneel; Naseem, Mohammed

doi:10.1109/csde50874.2020.9411534

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…These have presented the motivation to look at the frequency variable in detail on its impact on students' final performance COVID-19 era. Study [36] also analysed the impact of the two variables on students' performance using clustering. Studies [37][38][39] showed different methods to solve global optimization problems.…”

Section: A Different Measures Of Student Interactions In the Form Of ...mentioning

confidence: 99%

Statistical Methods for Data mining Mathematics students' online presence

Naseem

Reddy

Nand

2021

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)

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COVID-19 has caused major changes in every aspect of human endeavor, including efforts in the higher education sector. A sudden shift from face-to-face and blended settings to a completely online delivery mode has introduced changes to conventional teaching methods, and made learning rely heavily on technology and the Internet. Hence, students' online engagement with these tools has become even more important for their academic success. Therefore, there is a need to investigate the effects of various indicators of students' online presence on their academic performance. This paper explores the effectiveness of online presence in Higher Education Institutes, where COVID-19 has shifted the deliveries to online mode. The chosen indicator is frequency that will be adequately used to quantify the effectiveness of online presence on student performance in online mathematics courses. Statistical methods are used to measure the correlation and association between students' online presence indicators and their performance. As such, it would allow to build models to predict future outcomes or occurrences and student performances, with a major focus on mathematics and statistics courses. The results show that there is an increase in student online interaction in courses during COVID-19 era; however, it is consistent with the Online Measureable Presence Model (OMPM) model where frequency was the dominant indicator of student performance.

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Section: A Different Measures Of Student Interactions In the Form Of ...mentioning

confidence: 99%

Statistical Methods for Data mining Mathematics students' online presence

Naseem

Reddy

Nand

2021

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)

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“…In real-world problem-solving, optimization plays a vital role in addressing complex challenges across various domains, ranging from time-series prediction to devising efficient solutions [4]- [6]. Particularly in the education sector, optimal solutions are essential for diverse tasks, such as optimizing website response rates [7] or enhancing student performance [8], [9]. In Higher Education Institutes (HEIs), scheduling is a critical area that necessitates optimization, especially in the context of timetabling.…”

Section: Introductionmentioning

confidence: 99%

Preference-Based Stepping Ahead Firefly Algorithm for Solving Real-World Uncapacitated Examination Timetabling Problem

Nand,

Reddy,

Chaudhary

et al. 2024

IEEE Access

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Swarm-based intelligent optimization algorithms that employ principles of collective behavior have been gaining traction as a viable solution in optimization research. One area of optimization is the Examination Timetabling Problem (ETP), which presents a significant challenge in many Higher Education Institutes (HEIs). This paper proposes a novel approach to solving the Uncapacitated Examination Timetabling Problem (UETP) where a stepping-ahead mechanism is utilized with threshold acceptance in the Firefly Algorithm (FA). The proposed method improves exploration with the use of the steppingahead mechanism, while threshold acceptance allows for better exploitation of the search space. Initially, a neighborhood search mechanism is employed as the discretization of FA to improve solution quality, known as Kempe Chain-based neighborhoods. The proposed method is tested on 7 UETP problems, with the results showing comparative performance to the best solutions available in the literature for the Toronto exam timetabling dataset. The selection of seven problems is made with exams totaling less than 400, this allows to create a manageable yet representative benchmark. The study further extends the experiment to a real-world dataset collected from an HEI. The use of a real-world dataset allows us to see the potential of the algorithm and at the same time evaluate its performance under realistic conditions and resource constraints. The proposed stepping-ahead mechanism has the potential for use in other domains, such as robotics and engineering. Overall, this paper presents a new methodology for solving the UETP that has the potential to offer superior results when compared to existing approaches.

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“…In addition to, while dealing with complex real-world problems like designing solutions and/or time-series prediction, it is very important to optimize solutions either using selected optimizer ( Ghafori & Gharehchopogh, 2021 ; Gharehchopogh, 2022a ) or apply feature selection to remove noise ( Abdollahzadeh & Gharehchopogh, 2021 ; Mohmmadzadeh & Gharehchopogh, 2021 ; Naseri & Gharehchopogh, 2022 ; Gharehchopogh, Maleki & Dizaji, 2021b ). The education sector also requires optimal solutions for assorted task which range from website response rate ( Yunfeng, 2010 ) to student performance ( Nand, Chand & Naseem, 2020a ). In higher education institutes (HEI), one of the fundamental areas that need optimization is scheduling.…”

Section: Introductionmentioning

confidence: 99%

An introduction of preference based stepping ahead firefly algorithm for the uncapacitated examination timetabling

Nand¹,

Sharma²,

Chaudhary³

2022

PeerJ Computer Science

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In recent times, there has been a growing attention to intelligent optimization algorithms centred on swarm principles such as the firefly algorithm (FA). It was proposed for the continuous domain that mimics the attraction of fireflies to flashing light and has been used in discrete domains via modification. A discrete domain that is a major challenge in most higher education institutes (HEI) is examination timetabling. This article presents a new methodology based on FA for uncapacitated examination timetabling problems (UETP) where the proposed method is an extension of earlier work by the authors on the continuous domain. UETP is considered in this article as it is a university examination timetabling problem, which is still an active research area and has not been solved by FA algorithm as per authors knowledge. The proposed method concentrates on solving the initial solution using discrete FA where it consolidates the reordering of examinations and slots through a heuristic ordering known as neighborhood search. Three neighborhoods are employed in this research, where one is used during the initialization phase while two are utilized during solution improvement phase. Later, through preference parameters, a novel stepping ahead mechanism is used, which employs neighborhood searches built on previous searches. The proposed method is tested with 12 UETP problems where the preference based stepping ahead FA creates comparative results to the best ones available in the literature for the Toronto exam timetabling dataset. The results obtained are proof of concept at the preliminary stage and require further experiments on other educational datasets such as the second international timetable competition benchmark sets. The newly introduced preference based stepping ahead mechanism takes advantage of the current best solution space where it exploits the solution space for better solutions. This paves the way for researchers to utilize the mechanism in other domains such as robotics, etc.

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Analyzing students’ online presence in undergraduate courses using Clustering

Cited by 7 publications

References 12 publications

Statistical Methods for Data mining Mathematics students' online presence

Statistical Methods for Data mining Mathematics students' online presence

Preference-Based Stepping Ahead Firefly Algorithm for Solving Real-World Uncapacitated Examination Timetabling Problem

An introduction of preference based stepping ahead firefly algorithm for the uncapacitated examination timetabling

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