Analítiques d'aprenentatge: avaluació retrospectiva a nivell de curs

Autors/ores

DOI:

https://doi.org/10.7203/realia.31.25526

Paraules clau:

analítiques d’aprenentatge, compromís de l’estudiant, rendiment, retenció, educació superior

Resum

El concepte compromís de l’estudiant és controvertit. L’ús de perfils analítics de l’alumnat (PAA) per a mesurar el compromís de l’estudiant en el seu aprenentatge es complica tant per la falta d’acord sobre què és el que s’està mesurant realment com per la incomoditat o falta de confiança en- torn del que les dades acarades indiquen de manera creïble. Aquest repte es converteix en una cosa més complexa per l’escassa disponibilitat i la qüestionable precisió i fiabilitat de les dades. L’objectiu dels perfils analítics de l’alumnat és recopilar i compartir dades de participació inicial, que puguen utilitzar-se de manera predictiva per a millorar l’experiència i resultats posteriors. No obstant això, la majoria dels PAA recollits per les institucions d’educació superior són descriptius i, per tant, de limitada utilitat. Aquest article explora la credibilitat de dites PAA quan s’utilitzen al llarg del curs i són exclusivament descriptives. Aquest estudi de cas a petita escala ofereix una anàlisi de dades exhaustives recopilades dins i fora dels PAA per a una promoció de nivell 4 al llarg del curs acadè- mic 2019-20. El treball també empra dades sobre la finalització dels estudis d’aquella promoció, la qual cosa permet realitzar una anàlisi retrospectiva que proporciona més informació sobre la utilitat d’aquelles PAA en una etapa anterior de l’itinerari d’aquest alumnat. Tenint en compte els resultats reals d’aquests estudiants que van començar en 2019, apliquem la seua comprensió sobre què signi- fica ”compromís”, per a explicar els seus propis indicadors d’interacció i accions que podrien facilitar un compromís constructiu. Es van observar correlacions significatives entre l’ús dels recursos elec- trònics i els resultats dels estudiants, i es va descobrir que els indicadors de risc més significatius eren les pròrrogues, els suspensos i el no lliurament de treballs en el primer semestre del nivell 4, així com unes notes mitjanes inferiors al 39% al final del nivell 4. Entre les recomanacions de l’estudi s’inclou el fet de fomentar un accés millor i més segur als continguts de la bibliografia electrònica i oferir retroacció (feedback) a l’alumnat que mostra des del primer moment indicadors de risc.

Descàrregues

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Biografia de l'autor/a

Rachel Cliodhna Bassett-Dubsky, University of Northampton

Senior Lecturer - Childhood, Youth and Families Student Experience Lead - Faculty of Health, Education and Society

Referències

Agudo-Peregrina, A., Iglesias-Pradas, S., Conde-Gonzalez, M., & Hernandez-Garcia, A. (2014). 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, 31, 542–550. https://doi.org/10.1016/ j.chb.2013.05.031


Bassett-Dubsky, R. (2020). Student engagements project blog. [Blog post]. Retrieved from https://mypad.northampton.ac.uk/rdubsk/


Bassett-Dubsky, R. (2021). How has Covid-19 shifted how we support, recognise and measure student engagement? In S. Studente, S. Ellis, & B. Desai (Eds.), The Impact of COVID-19 on teaching and learning in Higher Education (pp. 151–173). Nova Science Publishers.


Beetham, H., Collier, A., Czerniewicz, L., Lamb, B., Lin, Y., Ross, J., … Wilson, A. (2022).    Surveillance Practices, Risks and Responses in the Post Pandemic University. Digital Culture & Education, 14(1), 16–37. Retrieved from https:// www.digitalcultureandeducation.com/volume-141-papers/beetham-2022


Blundell, R., Costa-Dias, M., Cribb, J., Joyce, R., Waters, T., Wernham, T., & Xu, X. (2022). Inequality and the Covid-19 crisis in the United Kingdom. Annual review of Economics, 14, 607–636. https://doi.org/10.1146/annurev-economics-051520-030252


Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: a systematic evidence map. International Journal of Educational Technology in Higher Education, 17 (2). https://doi.org/10.1186/s41239-019-0176-8


Braun, V., & Clarke, V. (2022). Thematic Analysis: A practical guide. London: Sage.

 

Broughan, C., & Prinsloo, P. (2020). ‘(Re)centring students in learning analytics: in conversation with Paulo Freire. Assessment & Evaluation in Higher Education, 45(4), 617–628. https://doi.org/10.1080/02602938.2019.1679716


Bunce, L., King, N., Saran, S., & Talib, N. (2021). Experiences of black and minority ethnic (BME) students in higher education: applying self-determination theory to understand the BME attainment gap. Studies in Higher Education, 46(3), 534–547. https://doi.org/ 10.1080/03075079.2019.1643305


De Freitas, S., Gibson, D., Plessis, C. D., Halloran, P., Williams, E., Ambrose, M., … Arnab, S. (2015). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology, 46(6), 1175–1188. https:// doi.org/10.1111/bjet.12212


Dommett, E., Gardner, B., & Van Tilburg, W. (2019). Staff and student views of lecture capture: a qualitative study. International Journal of Educational Technology in Higher Education, 16(23), 16–16. https://doi.org/10.1186/s41239-019-0153-2


Dyment, J., Stone, C., & Milthorpe, N.    (2020).    Beyond busy work: rethinking the measurement of online student engagement.Higher Education Research & Development, 39(7), 1440–1453. https://doi.org/10.1080/07294360.2020.1732879

Gravett, K., Kinchin, I., & Winstone, N. (2020). Frailty in transition? Troubling the norms, boundaries and limitations of transition theory and practice. Higher Education Research & Development, 39(6), 1169–1185.https://doi.org/10.1080/07294360.2020.1721442


GSU. (2019). Leading with predictive analytics . Retrieved from https://success.gsu.edu/ approach/


Hanover-Research. (2016, November). Learning Analytics for tracking student progress.Retrieved February 2023, 22, from https://www.imperial.edu/docs/research-planning/ 7932-learning-analytics-for-tracking-student-progress/file


Herodotou, C., Rienties, B., Boroowa, A., & Zdráhal, Z. (2019). A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective. Educational Technology Research and Development, 67 (2). http://dx.doi.org/10.1007/ s11423-019-09685-0


HESA. (2023, 01 31). Who’s studying in HE?: Personal characteristics . Retrieved from https:// www.hesa.ac.uk/data-and-analysis/students/whos-in-he/characteristics


Kika, C., Duan, Y., & Cao, G. (2016). Understanding the use and impact of learner analytics on Student Experience management in the UK Higher Education sector. In and others (Ed.), PACIS 2016 proceedings (pp. 54–54). Pacific Asia Conference on Information Systems. Retrieved from http://hdl.handle.net/10547/624450


Korhonen, V. (2012). Towards Inclusive Higher Education? - Outlining a Student-centred Counselling Framework for Strengthening Student Engagement. In S. Stolz & P. Gonon (Eds.), Challenges and Reforms in Vocational Education - Aspects of Inclusion and Exclusion (pp. 297–320). Bern: Peter Lang.


Korhonen, V., Mattsson, M., Inkinen, M., & Toom, A.    (2019).    Understanding the multidimensional nature of Student Engagement during the first year of Higher Education. Frontiers in Psychology, 10(1056), 1–15. https://doi.org/10.3389/fpsyg.2019.01056


Mathrani, A., Susnjak, T., Ramaswami, G., & Barczak, A. (2021). Perspectives on the challenges of generalisability, transparency and ethics in predictive learning analytics. Computers and Education Open, 2. https://doi.org/10.1016/j.caeo.2021.100060


Morgan, M. (2019, November). Presentation to SEDA conference.


Ofs. (2022). OfS sets news expectations for Student Outcomes . Retrieved from https:// www.officeforstudents.org.uk/news-blog-and-events/press-and-media/ofs-sets-new-expectations-for-student-outcomes/


Oldfield, J., Rodwell, J., Curry, L., & Marks, G. (2018). Psychological and demographic predictors of undergraduate non-attendance at university lectures and seminars. Journal of Further and Higher Education, 42(4), 509–523. https://doi.org/10.1080/ 0309877X.2017.1301404


Parkes, S., Benkwitz, A., Bardy, H., Myler, K., & Peters, J. (2020). Being more human: rooting learning analytics through resistance and reconnection with the values of higher education. Higher Education Research and Development, 39(1), 113–126. https://doi.org/10.1080/07294360.2019.1677569


Robertshaw, M. B., & Asher, A. (2019). Unethical Numbers? A Meta-Analysis of Library Learning Analytics Studies. Library Trends, 68(1), 76–101. https://doi.org/10.1353/ lib.2019.0031


Summers, R., Higson, H., & Moores, E. (2020). Measures of engagement in the first three weeks of higher education predict subsequent activity and attainment in first year undergraduate students: a UK case study. Assessment & Evaluation in Higher Education, 46(5), 821–836. https://doi.org/10.1080/02602938.2020.1822282


Susnjak, T., Suganya-Ramaswami, G., & Mathrani, A. (2022). Learning analytics dashboard: a tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education, 19(12). https://doi.org/10.1186/s41239-021-00313-7


Thorpe, A., Lukes, R., Bever, D., & He, Y.  (2016). The Impact of the Academic Library on Student Success: Connecting the Dots. Portal: Libraries and the Academy, 16(2), 373– 392. https://doi.org/10.1353/pla.2016.0027


Tobbell, J., Burton, R., Gaynor, A., Golding, B., Greenhough, K., Rhodes, C., & White, S. (2021). Inclusion in higher education: an exploration of the subjective experiences of students. Journal of Further and Higher Education, 45(2), 284–295. https://doi.org/ 10.1080/0309877X.2020.1753180


Venn, E., Park, J., Palle-Anderson, L., & Hejmadi, M. (2020). How do learning technologies impact on undergraduates’ emotional and cognitive engagement with their learning? Teaching in Higher Education, 28(4), 822–839. https://doi.org/10.1080/13562517.2020.1863349


Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https:// doi.org/10.1016/j.chb.2018.07.027


Zepke, N. (2018). Student engagement in neo-liberal times: what is missing? Higher Education Research & Development, 37 (2), 433–446. https://doi.org/10.1080/07294360.2017.1370440

Publicades

2023-07-26

Com citar

Bassett-Dubsky, R. C. (2023). Analítiques d’aprenentatge: avaluació retrospectiva a nivell de curs. Research in Education and Learning Innovation Archives, (31), 1–16. https://doi.org/10.7203/realia.31.25526
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