A Learning Analytic Approach to Modelling Student-Staff Interaction From Students’ Perception of Engagement Practices
The connection between student-staff interaction, students’ positive outcomes, and institutions has been widely studied as a key focus of research on student engagement and quality learning in higher education. In this study, a learning analytic approach is taken to establish a model for...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10387667/ |
| _version_ | 1797348720297115648 |
|---|---|
| author | Olufunke Oladipupo Seth Samuel |
| author_facet | Olufunke Oladipupo Seth Samuel |
| author_sort | Olufunke Oladipupo |
| collection | DOAJ |
| description | The connection between student-staff interaction, students’ positive outcomes, and institutions has been widely studied as a key focus of research on student engagement and quality learning in higher education. In this study, a learning analytic approach is taken to establish a model for student-staff interaction. Two African institutions are engaged in the analysis for data acquisition. The two student engagement datasets used in this study are acquired by survey approach using National Survey of Student Engagement Instrument from the student perspectives. An association rule mining technique with Frequent Pattern Growth algorithm is implemented to discover interesting associative patterns among the student engagement indicators in relation to two student engagement datasets. Structural equation modelling was then employed to investigate the discovered interesting associative relationships. This study evaluated 16 different student-staff interaction models using various fit indices to identify the most accurate predictor of student-staff interaction (SSI). The results suggest that poor quality interactions (QI), reflective and integrated learning (RI), and quantitative reasoning (QR) are key factors that influence the quality of SSI. The methodology and resulting validated models offer a unique contribution to the field and can inform the development of policies and best practices to enhance student engagement and improve learning outcomes in higher education. |
| first_indexed | 2024-03-08T12:10:03Z |
| format | Article |
| id | doaj.art-ccbf06a6bfbb47199f15c18647c12100 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T12:10:03Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ccbf06a6bfbb47199f15c18647c121002024-01-23T00:03:57ZengIEEEIEEE Access2169-35362024-01-0112103151033310.1109/ACCESS.2024.335244010387667A Learning Analytic Approach to Modelling Student-Staff Interaction From Students’ Perception of Engagement PracticesOlufunke Oladipupo0https://orcid.org/0000-0003-1309-0159Seth Samuel1https://orcid.org/0000-0001-9565-1700Department of Computer and Information Sciences, Covenant University, Ota, NigeriaDepartment of Computer and Information Sciences, Covenant University, Ota, NigeriaThe connection between student-staff interaction, students’ positive outcomes, and institutions has been widely studied as a key focus of research on student engagement and quality learning in higher education. In this study, a learning analytic approach is taken to establish a model for student-staff interaction. Two African institutions are engaged in the analysis for data acquisition. The two student engagement datasets used in this study are acquired by survey approach using National Survey of Student Engagement Instrument from the student perspectives. An association rule mining technique with Frequent Pattern Growth algorithm is implemented to discover interesting associative patterns among the student engagement indicators in relation to two student engagement datasets. Structural equation modelling was then employed to investigate the discovered interesting associative relationships. This study evaluated 16 different student-staff interaction models using various fit indices to identify the most accurate predictor of student-staff interaction (SSI). The results suggest that poor quality interactions (QI), reflective and integrated learning (RI), and quantitative reasoning (QR) are key factors that influence the quality of SSI. The methodology and resulting validated models offer a unique contribution to the field and can inform the development of policies and best practices to enhance student engagement and improve learning outcomes in higher education.https://ieeexplore.ieee.org/document/10387667/Learning analyticsstudent engagementstudent-staff interaction model |
| spellingShingle | Olufunke Oladipupo Seth Samuel A Learning Analytic Approach to Modelling Student-Staff Interaction From Students’ Perception of Engagement Practices IEEE Access Learning analytics student engagement student-staff interaction model |
| title | A Learning Analytic Approach to Modelling Student-Staff Interaction From Students’ Perception of Engagement Practices |
| title_full | A Learning Analytic Approach to Modelling Student-Staff Interaction From Students’ Perception of Engagement Practices |
| title_fullStr | A Learning Analytic Approach to Modelling Student-Staff Interaction From Students’ Perception of Engagement Practices |
| title_full_unstemmed | A Learning Analytic Approach to Modelling Student-Staff Interaction From Students’ Perception of Engagement Practices |
| title_short | A Learning Analytic Approach to Modelling Student-Staff Interaction From Students’ Perception of Engagement Practices |
| title_sort | learning analytic approach to modelling student staff interaction from students x2019 perception of engagement practices |
| topic | Learning analytics student engagement student-staff interaction model |
| url | https://ieeexplore.ieee.org/document/10387667/ |
| work_keys_str_mv | AT olufunkeoladipupo alearninganalyticapproachtomodellingstudentstaffinteractionfromstudentsx2019perceptionofengagementpractices AT sethsamuel alearninganalyticapproachtomodellingstudentstaffinteractionfromstudentsx2019perceptionofengagementpractices AT olufunkeoladipupo learninganalyticapproachtomodellingstudentstaffinteractionfromstudentsx2019perceptionofengagementpractices AT sethsamuel learninganalyticapproachtomodellingstudentstaffinteractionfromstudentsx2019perceptionofengagementpractices |