This article briefs about application of evidence-based learning analytics on performance enhancement of undergraduate students in proficiency skills as per current industrial employability needs.
Table of Contents
1. Introduction
2. Research Methodology
3. Results and Discussions
4. Conclusion
Research Objectives and Themes
The primary objective of this research is to evaluate the application of evidence-based learning analytics to enhance the proficiency skills of undergraduate engineering students in alignment with current industrial employability requirements through a cloud-based evaluation tool.
- Implementation of evidence-based learning analytics in engineering education.
- Continuous monitoring and evaluation of student proficiency levels.
- Comparative analysis of academic performance across seven diverse engineering disciplines.
- Utilization of cloud-based tools for data-driven skill assessment.
- Assessment of employability opportunities based on test performance indicators.
Excerpt from the Book
RESEARCH METHODOLOGY:
A total sample size of around 4000 test results of undergraduates of multi disciplines such as Aeronautical Engineering(AERO), Computer Science and Engineering(CSE), Information Technology(IT), Electronics and Communication Engineering(ECE), Electrical and Electronics Engineering(EEE), Mechanical Engineering(ME) and Civil Engineering(CIVIL). The test series contain 150 tests of 3 different skills such as Aptitude, Technical and English proficiency over four years of duration i.e., 2011 to 2015.
For the four year of engineering program, skill test score obtained by each test and cumulative score of each student in each department is collected and analyzed using Tableau software. This mode of analysis helps in various directions such as to encourage the evidence based learning analytics research; assessment of quality of engineering education; and attainment levels through these test series; and also the feedback to the organization about skewed standards of the engineering education practices.
Summary of Chapters
1. Introduction: This chapter introduces the importance of monitoring proficiency levels as an indicator for educational quality and proposes the implementation of a cloud-based employability tool.
2. Research Methodology: This section details the data collection process involving 4000 test results across seven disciplines over a four-year period and describes the use of Tableau for analytical purposes.
3. Results and Discussions: This chapter presents the statistical data, demonstrating performance variations among students and identifying which disciplines show higher employability potential based on test activity.
4. Conclusion: The final chapter summarizes that evidence-based learning analytics provides critical insights for policy makers to optimize learning environments and improve student employability outcomes.
Keywords
Learning analytics, proficiency tests, academic evaluation, skill development, employability, cloud-based tool, engineering education, data analysis, student performance, pedagogical research, industrial requirements, educational quality, Tableau, aptitude, technical proficiency.
Frequently Asked Questions
What is the core focus of this research paper?
The paper focuses on using evidence-based learning analytics to monitor and enhance the professional proficiency of undergraduate engineering students to meet industry standards.
What are the primary themes discussed in the study?
Key themes include continuous performance monitoring, the integration of cloud-based evaluation tools, and the analysis of test data to correlate academic performance with employability.
What is the main goal of the research?
The main goal is to demonstrate that implementing frequent, data-driven proficiency testing can serve as a quality indicator for engineering education and help improve student career outcomes.
Which scientific methodology is applied here?
The study employs a quantitative analysis of student performance data collected from 150 different tests over four years, visualized and processed using Tableau software.
What content is covered in the main body of the paper?
The main body covers the research methodology, detailed statistical tables regarding performance across seven disciplines, and graphical visualizations comparing student progress over time.
Which keywords define this work?
The work is characterized by terms such as learning analytics, employability, proficiency tests, and cloud-based evaluation.
How were the test scores scaled for evaluation?
Test scores were represented on a scale of 40 marks across three specific skill areas: Aptitude, Technical, and English proficiency.
What does the data suggest about Computer Science (CSE) students?
The data suggests that CSE students participate in a significantly higher number of tests, which the authors correlate with a greater scope for employability during recruitment cycles.
What is the significance of the cloud-based tool mentioned?
The Cloud Based Employability (CBE) tool acts as the primary mechanism for continuous training and evaluation, providing the necessary data points for the learning analytics model.
- Citar trabajo
- Venkata Aditya Nag Mannepalli (Autor), Dr. Myneni Madhu Bala (Autor), B. Padmaja (Autor), 2018, Learning Analytics On Cloud Based Employability Skill Test Series. Data On Proficiency Tests Performance Of Undergraduate Engineering Students, Múnich, GRIN Verlag, https://www.grin.com/document/436400
