Vivekanandan Suresh Kumar

Particle Swarm Optimization (PSO)-Based Clustering for Improving the Quality of Learning using Cloud Computing

Virtual Learning is a key enabler for giving equal opportunity to all throughout the globe. However, the pedagogical approach preferred by a group of learners may differ from another set of learners. By providing different pedagogical approaches through virtual learning, it is possible to satisfy the need of the learners, thereby improving the quality of learning. To identify the preference or choice of the pedagogy, the behavior of the learners is captured and analyzed. According to the understanding capability, the appropriate pedagogy is adopted for that learner. The conventional Learning Management System (LMS) plays a major role for achieving effective teaching and learning process. However, the conventional LMS fails to address the effective teaching and learning process by not providing the contents based on individual users ability. The proposed work mainly intends to capture the data from students, analyze and cluster the data based on their individual performances in terms of accuracy, efficiency and quality. The clustering process is carried out by employing the population-based metaheuristic algorithm of Particle Swarm Optimization (PSO). The simulation process is carried out by generating the data. The generated data is based on the real data collected from engineering undergraduate students. The proposed PSO-based clustering is compared with existing K-means algorithm for analyze the performance of inter cluster and intra cluster distances. Finally, the processed data is effectively stored in the Cloud resources using Hadoop Distributed File System (HDFS).

Unfolding Learning Analytics for Big Data

Educational applications, in general, treat disparate study threads as a singular entity, bundle pedagogical intervention and other student support services at a coarser level, and summatively assess final products of assessments. In this research, we propose an analytics framework where we closely monitor individual threads of study habits and assess study threads in an individual fashion to trace learning processes leading into assessment products. We developed customized intervention to target specific skills and nurture optimal study habits. The framework has been implemented in a system called SCALE (Smart Causal Analytics on LEarning). SCALE enables the tracking of students’ individual study threads towards multiple final study products. The large volume, multiple variety, and incessant flow of data classifies our work in the realms of big data analytics. We conducted a preliminary study using SCALE. The results show the ability of the system to track the evolution of competencies. We propose that explicitly supporting the development of a targeted set of competencies is one of the key tenets of Smart Learning Environments.

Big Data Learning Analytics: A New Perpsective

Learners’ attainment of academic knowledge in postsecondary institutions is predominantly expressed by summative approaches. Instead, recent advances in educational technology have hinted at a means to continuously measure learning attainment in terms of personalized learner competency, capacity, and effectiveness. Similarly, educational technology also offers guidelines to continuously measure instructional attainment in terms of instructional competency, instructional capacity, and instructional effectiveness. While accurate computational models that embody these attainments, educational and instructional, remain a distant and elusive goal, big data learning analytics approaches this goal by continuously observing study experiences and instructional practices at various levels of granularity, and by continually constructing and using models from these observations. This article offers a new perspective on learning and instructional attainments with big data analytics as the underlying framework, discusses approaches to this framework with evidences from the literature, and offers a case study that illustrates the need to pursue research directions arising from this new perspective.

SCALE: A Competence Analytics Framework

This paper introduces SCALE, a Smart Competence Analytics engine on LEarning, as a framework to implement content analysis in several learning domains and provide mechanisms to define proficiency and confidence metrics. SCALE’s ontological design plays a crucial role in centralizing and homogenizing disparate data from domain-specific parsers and ultimately from several learning domains. This paper shows how SCALE has been applied in the programming domain and reveals systematically how the work content of a student can be analyzed and converted to evidences to assess his/her proficiency in domain-specific competences and how SCALE can also analyze the student’s interaction with a learning activity and provide a confidence metric to assess his/her behavior as he/she culminates toward goal achievements.

Curricular and Learning Analytics: A Big Data Perspective

Analytics is about insights. Learning Analytics is about insights on factors such as capacity of learners, learning behaviour, predictability of learning concerns, and nurturing of cognitive aspects of learners, among others. Learning Analytics systems can engage learners to detect and appreciate insights generated by others, engage learners to investigate models on learning factors, and engage learners to create new insights. This chapter offers details of this vision for learning analytics, particularly in light of the ability to collect enormous amounts of data from students’ study episodes, wherever they happen to study using whatever resources they employ. Further, the chapter contends that learning analytics can also be used to make statements on the efficacy of a particular curriculum and recommend changes based on curricular insights.

Dynamic Learning Path Prediction — A Learning Analytics Solution

In the course of the last few years, many educational and research communities have been deeply invested in the development of learning analytics. Learning analytics measures the effectiveness and efficiency of learning environments, in order to understand the needs of learners and to improve the teaching process. The research presented in this paper uses Parallel Particle Swarm Optimization (PPSO) mechanism to analyze and predict a dynamic learning path for learners based on competence and meta-competence values observed in a learning environment. The proposed system is able to auto-configure and auto-customize itself to offer personalized and individualized instruction, and calculate an optimal learning pathway for learners. Furthermore, it provides on-demand and adaptive support for learners based on their needs. Experimental evaluations – carried out within a Java Programming course -- demonstrate the effectiveness of the proposed system.

Performance Analysis of Parallel Particle Swarm Optimization Based Clustering of Students

While accurate computational models that embody learning efficiency remain a distant and elusive goal, big data learning analytics approaches this goal by recognizing competency growth of learners, at various levels of granularity, using a combination of continuous, formative, and summative assessments. Our earlier research employed the conventional Particle Swarm Optimization (PSO) based clustering mechanism to cluster large numbers of learners based on their observed study habits and the consequent growth of subject knowledge competencies. This paper describes a Parallel Particle Swarm Optimization (PPSO) based clustering mechanism to cluster learners. Using a simulation study, performance measures of quality of clusters such as the Inter Cluster Distance, the Intra Cluster Distance, the processing time and the acceleration values are estimated and compared.

Case Studies of Industry-Academia Research Collaborations for Software Development with Agile

Successful industry-academia research collaborations (IARCs) in the software development area can be challenging. The literature identifies best practices in IARCs along with process frameworks with the aim of ensuring successful outcomes for both industry and academia, namely: funding opportunities for universities, training and employment possibilities for students, new knowledge leading to innovative products for industry, and on-time delivery of software benefiting the economy, the institution, and the community. This paper shows ways in which core principles of the project management approach, Agile, and the Scrum framework have been applied and have led to the success of three IARCs. In addition to IARCs’ common challenges, these case studies represented additional challenges as they were short-term software development projects accomplished by small geographically distributed teams. A report of the demographic, collaboration setting, and challenges, along with the lessons learned from the application of Agile and Scrum in these case studies will contribute to the body of knowledge in the field of IARCs. Using a qualitative and quantitative approach, five Agile/Scrum aspects for each project are assessed: product ownership, release, sprint, team, and technical health. Findings indicate several success factors directly linked to the application of the Agile principles and the Scrum framework. Specifically, early and frequent customer-centric software delivery, constant communications, responsiveness to change, and highly motivated individuals were key in terms of realizing the positive outcomes in spite of the obstacles inherent to IARCs. Cautions to this approach when applied in IARCs are reported along with solutions.

Swarm Intelligence (SI) based profiling and scheduling of big data applications

Personalization targets a users software, hardware, and QoS requirements at any given moment in the cloud environment for the big data applications. However, the individualization aims to target the daily needs of an individual user in a dynamic manner. The proposed research work aims to design a system which will be able to optimize users applications towards a specified target goal. Furthermore, it is integrated with a Particle Swarm Optimization (PSO) based application profiling and resource selection mechanism which comes from the family of Swarm Intelligence (SI). The proposed algorithms create an application profile template and preferred resource list for each submitted big data applications and select the cloud resources from the preferred resource list which is based on the application preferences and availability of cloud resources in an optimal manner. From the experimental results, it is evident that the proposed research work maximizes the application success ratio, scheduling success rate, utilization of cloud resources, and user satisfaction.

Learning Analytics in the Energy Industry: Measuring Competences in Emergency Procedures

Several major accidents in the oil and gas industry traced their source to deficient training resulting in serious injuries and even casualties along with extremely expensive damage to equipment and decrease in productivity. This paper presents a procedure evaluation/e-training tool called PeT to track the knowledge and confidence of trainees in emergency operating procedures. PeT was tested with two emergency procedures in an oil and gas company in Canada. A text-based knowledge test was implemented for each procedure. Each test consisted of multiple-choice questions. Answers were classified as perfectly correct, incomplete but correct, partially correct, mostly incorrect, and totally incorrect. The paper also describes the six-factor confidence model underlying the confidence computations in PeT: knowledge, reaction time, lingering, number of visits (revision), number of selections, and number of switching answers. Each confidence factor measures a specific aspect of the targeted behaviour in an emergency. The results of two experiments conducted in 2014 in an oil and gas company are also presented to show the types of analysis that PeT enables. A plan to move PeT into an interactive training environment to track the actions of operators in their work environment and translate their interaction into higher level competences is also briefly introduced.