#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Student engagement and wellbeing over time at a higher education institution


Autoři: Chris A. Boulton aff001;  Emily Hughes aff002;  Carmel Kent aff001;  Joanne R. Smith aff002;  Hywel T. P. Williams aff001
Působiště autorů: Computer Science, University of Exeter, Exeter, United Kingdom aff001;  School of Psychology, University of Exeter, Exeter, United Kingdom aff002
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0225770

Souhrn

Student engagement is an important factor for learning outcomes in higher education. Engagement with learning at campus-based higher education institutions is difficult to quantify due to the variety of forms that engagement might take (e.g. lecture attendance, self-study, usage of online/digital systems). Meanwhile, there are increasing concerns about student wellbeing within higher education, but the relationship between engagement and wellbeing is not well understood. Here we analyse results from a longitudinal survey of undergraduate students at a campus-based university in the UK, aiming to understand how engagement and wellbeing vary dynamically during an academic term. The survey included multiple dimensions of student engagement and wellbeing, with a deliberate focus on self-report measures to capture students’ subjective experience. The results show a wide range of engagement with different systems and study activities, giving a broad view of student learning behaviour over time. Engagement and wellbeing vary during the term, with clear behavioural changes caused by assessments. Results indicate a positive interaction between engagement and happiness, with an unexpected negative relationship between engagement and academic outcomes. This study provides important insights into subjective aspects of the student experience and provides a contrast to the increasing focus on analysing educational processes using digital records.

Klíčová slova:

Learning – Human learning – Behavior – Surveys – Motivation – Lectures – Teaching methods – Happiness


Zdroje

1. Kahn PE. Theorising student engagement in higher education. British Educational Research Journal. 2014;40(6):1005–18.

2. Krause KL, Coates H. Students’ engagement in first‐year university. Assessment & Evaluation in Higher Education. 2008;33(5):493–505.

3. Zhu E. Interaction and cognitive engagement: An analysis of four asynchronous online discussions. Instructional Science. 2006;34(6):451.

4. Kuzilek J, Hlosta M, Herrmannova D, Zdrahal Z, Wolff A. OU Analyse: Analysing at-risk students at The Open University. Learning Analytics Review. 2015;LAK15(1):1–16.

5. Cerezo R, Sánchez-Santillán M, Paule-Ruiz MP, Núñez JC. Students' LMS interaction patterns and their relationship with achievement: A case study in higher education. Computers & Education. 2016;96:42–54.

6. Pascarella ET, Seifert TA, Blaich C. How Effective are the NSSE Benchmarks in Predicting Important Educational Outcomes? Change: The Magazine of Higher Learning. 2010;42(1):16–22.

7. Kuh GD, Cruce TM, Shoup R, Kinzie J, Gonyea RM. Unmasking the Effects of Student Engagement on First-Year College Grades and Persistence. The Journal of Higher Education. 2008;79(5):540–63.

8. Boulton CA, Kent C, Williams HTP. Virtual learning environment engagement and learning outcomes at a ‘bricks-and-mortar’ university. Computers & Education. 2018;126:129–42.

9. Agudo-Peregrina ÁF, Iglesias-Pradas S, Conde-González MÁ, Hernández-García Á. Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior. 2014;31:542–50.

10. Rienties B, Toetenel L. The impact of learning design on student behaviour, satisfaction and performance: A cross-institutional comparison across 151 modules. Computers in Human Behavior. 2016;60:333–41.

11. Joksimović S, Gašević D, Loughin TM, Kovanović V, Hatala M. Learning at distance: Effects of interaction traces on academic achievement. Computers & Education. 2015;87:204–17.

12. Na KS, Tasir Z, editors. Identifying at-risk students in online learning by analysing learning behaviour: A systematic review. 2017 IEEE Conference on Big Data and Analytics (ICBDA); 2017 16–17 Nov. 2017.

13. Cambruzzi W, Rigo SJ, Barbosa JLV. Dropout Prediction and Reduction in Distance Education Courses with the Learning Analytics Multitrail Approach. j-jucs. 2015;21(1):23–47.

14. Wang R, Chen F, Chen Z, Li T, Harari G, Tignor S, et al. StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing; Seattle, Washington. 2632054: ACM; 2014. p. 3–14.

15. Kent C, Boulton CA, Williams HTP. Towards Measurement of the Relationship between Student Engagement and Learning Outcomes at a Bricks-and-Mortar University. Sixth Multimodal Learning Analytics (MMLA) Workshop and the Second Cross-LAK Workshop co-located with 7th International Learning Analytics and Knowledge Conference (LAK 2017); Vancouver, Canada2017.

16. Wang R, Harari G, Hao P, Zhou X, Campbell AT. SmartGPA: how smartphones can assess and predict academic performance of college students. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing; Osaka, Japan. 2804251: ACM; 2015. p. 295–306.

17. Cochran JD, Campbell SM, Baker HM, Leeds EM. The Role of Student Characteristics in Predicting Retention in Online Courses. Research in Higher Education. 2014;55(1):27–48.

18. de Freitas S, Gibson D, Du Plessis C, Halloran P, Williams E, Ambrose M, et al. Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology. 2015;46(6):1175–88.

19. Dekker GW, Pechenizkiy M, Vleeshouwers JM. Predicting Students Drop Out: A Case Study. 2nd Educational Data Mining; Cordoba, Spain: ERIC; 2009.

20. Romero C, Ventura S. Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2013;3(1):12–27.

21. Borden VMH, Coates H. Learning Analytics as a Counterpart to Surveys of Student Experience. New Directions for Higher Education. 2017;2017(179):89–102.

22. Sønderlund A, Hughes E, Smith J. The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology. 2019;50(5):2594–618.

23. Sclater N, Peasgood A, Mullan J. Learning Analytics in Higher Education: A review of UK and international practice. Joint Information of Systems Committee (JISC). CC by 4.0 Licence: UK; 2016.

24. Sclater N, Mullan J. Learning analytics and student success: Assessing the evidence. Joint Information of Systems Committee (JISC). CC by 4.0 License: UK; 2016.

25. Viberg O, Hatakka M, Bälter O, Mavroudi A. The current landscape of learning analytics in higher education. Computers in Human Behavior. 2018;89:98–110.

26. Shum SB, Crick RD, editors. Learning dispositions and transferable competencies: pedagogy, modelling and learning analytics. Proceedings of the 2nd international conference on learning analytics and knowledge; 2012: ACM.

27. Tempelaar DT, Rienties B, Giesbers B. In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior. 2015;47:157–67.

28. Tempelaar D, Rienties B, Mittelmeier J, Nguyen Q. Student profiling in a dispositional learning analytics application using formative assessment. Computers in Human Behavior. 2018;78:408–20.

29. Tempelaar D, Rienties B, Nguyen Q. A multi-modal study into students’ timing and learning regulation: time is ticking. Interactive Technology and Smart Education. 2018;15(4):298–313.

30. D’Mello S, Graesser A. Dynamics of affective states during complex learning. Learning and Instruction. 2012;22(2):145–57.

31. Pardos ZA, Baker RS, San Pedro MO, Gowda SM, Gowda SM. Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End-of-Year Learning Outcomes. Journal of Learning Analytics. 2014;1(1):107–28.

32. Saeb S, Lattie EG, Schueller SM, Kording KP, Mohr DC. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ. 2016;4:e2537. doi: 10.7717/peerj.2537 28344895

33. Deci EL, Ryan RM. Hedonia, eudaimonia, and well-being: an introduction. Journal of Happiness Studies. 2008;9(1):1–11.

34. Diener E, Heintzelman SJ, Kushlev K, Tay L, Wirtz D, Lutes LD, et al. Findings all psychologists should know from the new science on subjective well-being. Canadian Psychology/Psychologie canadienne. 2017;58(2):87–104.

35. Rinn AN. Trends Among Honors College Students: An Analysis by Year in School. Journal of Secondary Gifted Education. 2005;16(4):157–67.

36. Plominski AP, Burns LR. An Investigation of Student Psychological Wellbeing: Honors Versus Nonhonors Undergraduate Education. Journal of Advanced Academics. 2018;29(1):5–28.

37. Pietarinen J, Soini T, Pyhältö K. Students’ emotional and cognitive engagement as the determinants of well-being and achievement in school. International Journal of Educational Research. 2014;67:40–51.

38. Thorley C. Not By Degrees: Not by degrees: Improving student mental health in the UK’s universities. IPPR; 2017.

39. Houghton A-M, Anderson J. Embedding mental wellbeing in the curriculum: maximising success in higher education. Higher Education Academy,(forthcoming). 2017;68.

40. Lam SF, Jimerson SR. Exploring student engagement in schools internationally: Consultation paper. Chicago, IL: International School Psychologist Association; 2008.

41. Herodotou C, Rienties B, Boroowa A, Zdrahal Z, Hlosta M. A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective. Educational Technology Research and Development. 2019;67(5):1273–306.

42. Lawther S, Foster E, Mutton J, Kerrigan M. Can the Use of Learning Analytics Encourage Positive Student Behaviours? In: Janes G, Nutt D, Taylor P, editors. Student Behaviour and Positive Learning Cultures: SEDA; 2016. p. 13–21.

43. Richardson JTE. The role of response biases in the relationship between students’ perceptions of their courses and their approaches to studying in higher education. British Educational Research Journal. 2012;38(3):399–418.

44. Gasevic D, Jovanovic J, Pardo A, Dawson S. Detecting learning strategies with analytics: Links with self-reported measures and academic performance. Journal of Learning Analytics. 2017;4(2):113–28-–28.

45. Winne PH, Jamieson-Noel D. Exploring students’ calibration of self reports about study tactics and achievement. Contemporary Educational Psychology. 2002;27(4):551–72.

46. Zhou M, Winne PH. Modeling academic achievement by self-reported versus traced goal orientation. Learning and Instruction. 2012;22(6):413–9.


Článok vyšiel v časopise

PLOS One


2019 Číslo 11
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Získaná hemofilie - Povědomí o nemoci a její diagnostika
nový kurz

Eozinofilní granulomatóza s polyangiitidou
Autori: doc. MUDr. Martina Doubková, Ph.D.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#