Use of Microsoft Excel for Data Collection and Processing to Predict Students' Performance in EDM.
Saved in:
| Title: | Use of Microsoft Excel for Data Collection and Processing to Predict Students' Performance in EDM. |
|---|---|
| Authors: | Dol, Sunita M.1 sunita_aher@yahoo.com, Jawandhiya, P. M.2 pmjawandhiya@gmail.com, Satav, Pravin R.3 prsatav@gmail.com |
| Source: | Journal of Engineering Education Transformations. 2024 Special Issue, Vol. 37, p22-43. 22p. |
| Subject Terms: | *Electronic data processing, *Computer science, Data mining, Acquisition of data, Evidence gaps, Computer engineering, Electronic spreadsheets |
| Abstract: | Educational Data Mining (EDM) referes to the research designed to classify, analyze, and predict the students' academic performance from the data collected from educational setting. Data collection and data processing are an important task in any research such as EDM. In this research article, data collection and data processing task are explained in detailed to build the model for predicting students' performance and provide the recommendation in Educational Data Mining. In data collection step, we have collected the result ledgers in PDF form related to Four Year Computer Science and Engineering (CSE) course from university. The PDF ledgers for two academic years 2014-15 and 2015-16 of Four Years - First Year, Second Year, Third Year, and Final Year are downloaded from site http://www.sus.ac.in/examination/Online-Result-(Ledger) or https://su.digitaluniversity.ac/Content.aspx?ID=29445 to prepare the dataset to predict students' performance in Educational Data Mining (EDM). In current study, Syllabus structure of Four Year course of Computer Science and Engineering, Credit system pattern, attributes required for preparing dataset, and types of assessment methods such as Types of assessment methods such as Theory + Practical, Theory + Practical + Practical Oral Exam (POE), Practical + POE, Practical + OE, Practical, Term Work, and Theory are explained in detailed. So original data downloaded from university site for two academic years 2014-15 and 2015-16 of Four Years CSE course from Sem-I to Sem-VIII is prepared with the help of Excel and contain approximately 10,616 students data with 544 number of attributes. For data processing, Microsoft Excel is used. Microsoft Excel features such as Text to Column -- Delimited, Text to Column - Fixed width, Filter, and Conditional Formatting -- Highlight Cells Rules -- Text that contains -- are considered for preparation of dataset. Also various functions such as SUM, IF, COUNTIF, MOD and % are employed for processing the data. After data processing step, final dataset for two academic years 2014-15 and 2015-16 from Sem-I to Sem-VIII consists of 6906 students data with 970 number of attributes. In addition to the data collection and processing, research gaps related to the dataset size, etc. are also identified and mentioned the same in this article. These two steps - data collection and processing discussed in detailed in this research article will help the researcher working in EDM to prepare the dataset to build the model so that more work in education sector related to students' performance can be carried out to improve the teaching-learning process. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Engineering Education Transformations is the property of Rajarambapu Institute of Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: ehh DbLabel: Education Research Complete An: 177982863 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Use of Microsoft Excel for Data Collection and Processing to Predict Students' Performance in EDM. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dol%2C+Sunita+M%2E%22">Dol, Sunita M.</searchLink><relatesTo>1</relatesTo><i> sunita_aher@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Jawandhiya%2C+P%2E+M%2E%22">Jawandhiya, P. M.</searchLink><relatesTo>2</relatesTo><i> pmjawandhiya@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Satav%2C+Pravin+R%2E%22">Satav, Pravin R.</searchLink><relatesTo>3</relatesTo><i> prsatav@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Engineering+Education+Transformations%22">Journal of Engineering Education Transformations</searchLink>. 2024 Special Issue, Vol. 37, p22-43. 22p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Evidence+gaps%22">Evidence gaps</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+engineering%22">Computer engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+spreadsheets%22">Electronic spreadsheets</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Educational Data Mining (EDM) referes to the research designed to classify, analyze, and predict the students' academic performance from the data collected from educational setting. Data collection and data processing are an important task in any research such as EDM. In this research article, data collection and data processing task are explained in detailed to build the model for predicting students' performance and provide the recommendation in Educational Data Mining. In data collection step, we have collected the result ledgers in PDF form related to Four Year Computer Science and Engineering (CSE) course from university. The PDF ledgers for two academic years 2014-15 and 2015-16 of Four Years - First Year, Second Year, Third Year, and Final Year are downloaded from site http://www.sus.ac.in/examination/Online-Result-(Ledger) or https://su.digitaluniversity.ac/Content.aspx?ID=29445 to prepare the dataset to predict students' performance in Educational Data Mining (EDM). In current study, Syllabus structure of Four Year course of Computer Science and Engineering, Credit system pattern, attributes required for preparing dataset, and types of assessment methods such as Types of assessment methods such as Theory + Practical, Theory + Practical + Practical Oral Exam (POE), Practical + POE, Practical + OE, Practical, Term Work, and Theory are explained in detailed. So original data downloaded from university site for two academic years 2014-15 and 2015-16 of Four Years CSE course from Sem-I to Sem-VIII is prepared with the help of Excel and contain approximately 10,616 students data with 544 number of attributes. For data processing, Microsoft Excel is used. Microsoft Excel features such as Text to Column -- Delimited, Text to Column - Fixed width, Filter, and Conditional Formatting -- Highlight Cells Rules -- Text that contains -- are considered for preparation of dataset. Also various functions such as SUM, IF, COUNTIF, MOD and % are employed for processing the data. After data processing step, final dataset for two academic years 2014-15 and 2015-16 from Sem-I to Sem-VIII consists of 6906 students data with 970 number of attributes. In addition to the data collection and processing, research gaps related to the dataset size, etc. are also identified and mentioned the same in this article. These two steps - data collection and processing discussed in detailed in this research article will help the researcher working in EDM to prepare the dataset to build the model so that more work in education sector related to students' performance can be carried out to improve the teaching-learning process. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Engineering Education Transformations is the property of Rajarambapu Institute of Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=177982863 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.16920/jeet/2024/v37is2/24020 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 22 Subjects: – SubjectFull: Electronic data processing Type: general – SubjectFull: Computer science Type: general – SubjectFull: Data mining Type: general – SubjectFull: Acquisition of data Type: general – SubjectFull: Evidence gaps Type: general – SubjectFull: Computer engineering Type: general – SubjectFull: Electronic spreadsheets Type: general Titles: – TitleFull: Use of Microsoft Excel for Data Collection and Processing to Predict Students' Performance in EDM. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dol, Sunita M. – PersonEntity: Name: NameFull: Jawandhiya, P. M. – PersonEntity: Name: NameFull: Satav, Pravin R. IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 01 Text: 2024 Special Issue Type: published Y: 2024 Identifiers: – Type: issn-print Value: 23492473 Numbering: – Type: volume Value: 37 Titles: – TitleFull: Journal of Engineering Education Transformations Type: main |
| ResultId | 1 |
