Academic Analytics Implemented for Students Performance in Terms of Canonical Correlation Analysis and Chi-Square Analys
In this research study, we were interested to test the significant association between selected variables which otherwise called as invisible and have indirect impact on the performance of the students. We have devised out our own dataset for the experime
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Abstract In this research study, we were interested to test the significant association between selected variables which otherwise called as invisible and have indirect impact on the performance of the students. We have devised out our own dataset for the experimental purpose. Our study has made these variables and their relationship visible. The results enable us to determine characteristics of learning environment related to performance. Keywords Data mining
Statistical analysis Patterns
1 Introduction Academic analytics is one branch of modern day’s data analysis which uses statistical analysis and data mining methods to reveal and recognize hidden patterns in vast educational databases [1–6]. Such patterns enable us to throw better light on educational aspects related to student behavior, prognostication, student-centric learning, remedial aspects, and learning outcome with high accuracy. This will
A. Muley (&) School of Mathematical Sciences, S.R.T.M. University, Nanded 431606, Maharashtra, India e-mail: [email protected] P. Bhalchandra P. Wasnik School of Computational Sciences, S.R.T.M. University, Nanded 431606, Maharashtra, India e-mail: [email protected] P. Wasnik e-mail: [email protected] M. Joshi School of Educational Sciences, S.R.T.M. University, Nanded 431606, Maharashtra, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 D.K. Mishra et al. (eds.), Information and Communication Technology, Advances in Intelligent Systems and Computing 625, https://doi.org/10.1007/978-981-10-5508-9_26
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definitely increase standards of Indian higher educational system [6]. Due to digitization and effective use of computers, IT and ICT technologies, all educational organizations, institutions, and universities have generated and stored large data in their databases [7–13]. This data can be a key source for futuristic decision making processes if it is being processed through academic analytics. We took it as a challenge to see all the business intelligence, patterns, correlations, and rules embedded in this data. Our work is an interdisciplinary work undertaken by three schools of our university as performance analysis shares sphere with educational pedagogies, statistics, and computer-enabled technologies. The academic analytics was implemented using SPSS software [14, 15]. A closed questionnaire with predefined answers was used for data gathering [16] on A4 size single-sided paper sheet. Performance-related economical, social, and emotional attributes of this questionnaire were selected with the help of School of Educational Sciences and as per theory of Pritchard and Wilson [16, 17]. The questionnaire was modified number of times to reduce the complexity of understanding as well as to increase simplicity of answering. It was tested on subset of students after every revision. An Excel sheet was prepared for the answers using code such as 0, 1, 2. The confidential issues in datasets were properly addressed as dataset carried personal information
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