Critical Lens in Learning Analytics Research: A Systematic Literature Review
Resumo
This paper describes a systematic literature review that aims to answer the following research question: How has critical theory been used in learning analytics research? Nine previous literature review and fifteen studies were analyzed. Results showed that none of the previous literature reviews considered critical theory as a component of their analyzes and the topics of algorithm bias, ethics, justice and prediction are among the most studied on works that used Critical lens in Learning Analytics. Considerations are drawn to support future works that promotes the use of Critical lens in Learning Analytics and in the design of adaptive systems for learning and teaching.
Palavras-chave:
Learning Analytics, Critical Theory, Algorithm Bias, Ethics, Justice, Prediction
Referências
Aguilar, S. J. (2018a). Learning Analytics: at the Nexus of Big Data, Digital Innovation, and Social Justice in Education. Tech Trends, 62, 37-45.
Aguilar, S. J. (2018b). Examining the Relationship Between Comparative and Self-Focused Academic Data Visualizations in At-Risk Students’ Academic Motivation. Journal of Research on Technology in Education, 50(1), 84-103.
Alfy, S. E., Gómez, J. M., Dani, A. (2019). Exploring the benefits and challenges of learning analytics in higher education institutions: a systematic literature review. Information Discovery and Delivery, 47(1), 25-34.
Anyon, J. (2008). “Introduction: Critical Social Theory, Educational Research, and Intellectual Agency”. In J. Anyon (Ed.), Theory and Educational Research, p. 1-24. New York: Routledge.
Avella, J. T., Kebritchi, M., Nunn, S. G., Kanai, T. (2016). Learning Analytics Methods, Benefits and Challenges in Higher Education: A Systematic Literature Review. Online Learning, 20(2), 13-29.
Banihashem, S. K., Aliabadi, K., Ardakani, S. P., Delaver, A., Ahmadabadi, M. N. (2018). Learning Analytics: A Systematic Literature Review. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 9(2), 1-10.
Benson, V., Filippaios, F. (2019). The role of learning analytics in networking for business and leisure: A study of culture and gender differences in social platform users. Computers in Human Behavior, 92, 613-624.
Coelho, O. B., Silveira, I. F. (2017). “Deep Learning applied to Learning Analytics and Educational Data Mining: A Systematic Literature Review”. In XXVIII Simpósio Brasileiro de Informática na Educação (SBIE 2017), p. 143-152.
Crenshaw, K. (1991). Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color. Stanford Law Review, 43(6), pages 1241-1299.
Espinoza, O. (2007). Solving the equity-equality conceptual dilemma: a new model for analysis of the educational process. Educational Research, 49(4), 343-363.
Ferguson R. (2012). Learning analytics: drivers, developments and challenges. Int J Tech Enhanced Learn (IJTEL), 4(5/6), 304-317.
Freire, P. (1972). Pedagogy of the oppressed. New York: Herder and Herder.
Gasevic, D., Jovanovic, J., Pardo, A., & Dawson, S. (2017). Detecting learning strategies with analytics: Links with self-reported measures and academic performance. Journal of Learning Analytics, 4(2), 113–128.
Gasevic, D., Dawson, S., Rogers, R. and Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. In The Internet and Higher Education, 68-84. Elsevier.
Johnson, J. A. (2017). Ethics and Justice in Learning Analytics. New Directions in Higher Education, 179, 77 – 87.
Jones, K. M., McCoy, C. (2018). Reconsidering data in learning analytics: Opportunities for critical research. Learning, Media and Technology, 1-19.
Jones, K. M., McCoy, C. (2019). “Ethics in Praxis: Social-Technical Integration Research in Learning Analytics”. In Companion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19), Fairness and Equity in Learning Analytics Systems Workshop, p. 5-14.
Knox, J. (2017). Data Power in Education: Exploring Critical Awareness with the “Learning Analytics Report Card”. Television and New Media, 18(8), 743-752.
Lang, D. (2019). “Strategic Omission and Risk Aversion: A Bias-Reliability Tradeoff”. In Companion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19), Fairness and Equity in Learning Analytics Systems Workshop, p. 20-28.
Leavy, S. (2018). “Gender Bias in Artificial Intelligence: The Need for Diversity and Gender Theory in Machine Learning”. In 1st International Workshop on Gender Equality in Software Engineering, p. 14-16.
Lincoln, Y. S., Lynham, S. A., Guba, E. G. (2011). Paradigmatic controversies, contradictions, and emerging confluences, revisited. In Denzin, N. K., Lincoln, Y. S. (Eds.), The SAGE handbook of qualitative research, p. 97-128. LA: SAGE.
Mangoroska, K., Giannakos, M. (2018). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions of Learning Technologies (Early Access), 1-19.
McKay, T. A., Grom, G., Koester, B. (2018). Categorization, Intersectionality, and Learning Analytics. In Companion Proceeding of the 8th International Conference on Learning Analytics, p. 1-5.
McKenna, K., Folkestad, J. E., Moraes, M. C. (2019). “Reflections of Visual Form Learning Analytics: Spaced Retrieval Practice Activity”. In Companion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19), Practitioner Track, p. 20-25.
Meaney, M., Fikes, T. (2019) “Early-adopter Iteration Bias and Research-praxis Bias in the Learning Analytics Ecosystem”. In Companion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19), Fairness and Equity in Learning Analytics Systems Workshop, p. 14-20.
Moraes, R., & Galiazzi, M. C. (2007). Análise Textual Discursiva. Ijuí: E. Unijuí.
Petticrew, M., & Roberts, H. (2006). Systematic Reviews in the Social Sciences: a practical guide. Oxford: Blackwell.
Prinsloo, P., & Slade, S. (2016). Student vulnerability, agency, and learning analytics: An exploration. Journal of Learning Analytics, 3(1), 159–182.
Prinsloo, P., Slade, S. (2017). Big Data, Higher Education and Learning Analytics, Beyond Justice and Ethics of Care. B. Kei Daniel (ed.), Big Data and Learning Analytics in Higher Education, p. 109-124.
Rizvi, S., Rienties, B., Khoja, S. A. (2019). A role of demographics in online learning; A decision tree based approach. Computers & Education, 137, 32-47.
Selwyn, N. (2014). Distrusting educational technology. Critical questions for changing times. New York: Routledge.
Shibani, A., Knight, S., and Shum, S. B. (2019). “Contextualizable Learning Analytics Design: A Generic Model, and Writing Analytics Evaluations”. In Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19), p. 210-219.
Si Na, K., Tasir, Z. (2017). “A Systematic Review of Learning Analytics Intervention Contributing to Student Success in Online Learning”. In 2017 International Conference on Learning and Teaching in Computing and Engineering, p. 62-68.
Tsai,Y., Gasevic, D. (2017). “Learning Analytics in Higher Education – Challenges and Policies: A Review of Eight Learning Analytics Policies”. In Proceedings of the 7th International Conference on Learning Analytics & Knowledge (LAK’17), p. 233-242.
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.
Vieira, C., Parsons, P., Byrd, V. (2018). Visual learning analytics of educational data: A systematic literature review and research agenda. Computers & Education, 122, 119-135.
West. D., Huijser, H., Heath, D. (2016). Putting an ethical lens on learning analytics. Education Tech Research Dev, 64, 903–922.
Aguilar, S. J. (2018b). Examining the Relationship Between Comparative and Self-Focused Academic Data Visualizations in At-Risk Students’ Academic Motivation. Journal of Research on Technology in Education, 50(1), 84-103.
Alfy, S. E., Gómez, J. M., Dani, A. (2019). Exploring the benefits and challenges of learning analytics in higher education institutions: a systematic literature review. Information Discovery and Delivery, 47(1), 25-34.
Anyon, J. (2008). “Introduction: Critical Social Theory, Educational Research, and Intellectual Agency”. In J. Anyon (Ed.), Theory and Educational Research, p. 1-24. New York: Routledge.
Avella, J. T., Kebritchi, M., Nunn, S. G., Kanai, T. (2016). Learning Analytics Methods, Benefits and Challenges in Higher Education: A Systematic Literature Review. Online Learning, 20(2), 13-29.
Banihashem, S. K., Aliabadi, K., Ardakani, S. P., Delaver, A., Ahmadabadi, M. N. (2018). Learning Analytics: A Systematic Literature Review. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 9(2), 1-10.
Benson, V., Filippaios, F. (2019). The role of learning analytics in networking for business and leisure: A study of culture and gender differences in social platform users. Computers in Human Behavior, 92, 613-624.
Coelho, O. B., Silveira, I. F. (2017). “Deep Learning applied to Learning Analytics and Educational Data Mining: A Systematic Literature Review”. In XXVIII Simpósio Brasileiro de Informática na Educação (SBIE 2017), p. 143-152.
Crenshaw, K. (1991). Mapping the Margins: Intersectionality, Identity Politics, and Violence against Women of Color. Stanford Law Review, 43(6), pages 1241-1299.
Espinoza, O. (2007). Solving the equity-equality conceptual dilemma: a new model for analysis of the educational process. Educational Research, 49(4), 343-363.
Ferguson R. (2012). Learning analytics: drivers, developments and challenges. Int J Tech Enhanced Learn (IJTEL), 4(5/6), 304-317.
Freire, P. (1972). Pedagogy of the oppressed. New York: Herder and Herder.
Gasevic, D., Jovanovic, J., Pardo, A., & Dawson, S. (2017). Detecting learning strategies with analytics: Links with self-reported measures and academic performance. Journal of Learning Analytics, 4(2), 113–128.
Gasevic, D., Dawson, S., Rogers, R. and Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. In The Internet and Higher Education, 68-84. Elsevier.
Johnson, J. A. (2017). Ethics and Justice in Learning Analytics. New Directions in Higher Education, 179, 77 – 87.
Jones, K. M., McCoy, C. (2018). Reconsidering data in learning analytics: Opportunities for critical research. Learning, Media and Technology, 1-19.
Jones, K. M., McCoy, C. (2019). “Ethics in Praxis: Social-Technical Integration Research in Learning Analytics”. In Companion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19), Fairness and Equity in Learning Analytics Systems Workshop, p. 5-14.
Knox, J. (2017). Data Power in Education: Exploring Critical Awareness with the “Learning Analytics Report Card”. Television and New Media, 18(8), 743-752.
Lang, D. (2019). “Strategic Omission and Risk Aversion: A Bias-Reliability Tradeoff”. In Companion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19), Fairness and Equity in Learning Analytics Systems Workshop, p. 20-28.
Leavy, S. (2018). “Gender Bias in Artificial Intelligence: The Need for Diversity and Gender Theory in Machine Learning”. In 1st International Workshop on Gender Equality in Software Engineering, p. 14-16.
Lincoln, Y. S., Lynham, S. A., Guba, E. G. (2011). Paradigmatic controversies, contradictions, and emerging confluences, revisited. In Denzin, N. K., Lincoln, Y. S. (Eds.), The SAGE handbook of qualitative research, p. 97-128. LA: SAGE.
Mangoroska, K., Giannakos, M. (2018). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions of Learning Technologies (Early Access), 1-19.
McKay, T. A., Grom, G., Koester, B. (2018). Categorization, Intersectionality, and Learning Analytics. In Companion Proceeding of the 8th International Conference on Learning Analytics, p. 1-5.
McKenna, K., Folkestad, J. E., Moraes, M. C. (2019). “Reflections of Visual Form Learning Analytics: Spaced Retrieval Practice Activity”. In Companion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19), Practitioner Track, p. 20-25.
Meaney, M., Fikes, T. (2019) “Early-adopter Iteration Bias and Research-praxis Bias in the Learning Analytics Ecosystem”. In Companion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19), Fairness and Equity in Learning Analytics Systems Workshop, p. 14-20.
Moraes, R., & Galiazzi, M. C. (2007). Análise Textual Discursiva. Ijuí: E. Unijuí.
Petticrew, M., & Roberts, H. (2006). Systematic Reviews in the Social Sciences: a practical guide. Oxford: Blackwell.
Prinsloo, P., & Slade, S. (2016). Student vulnerability, agency, and learning analytics: An exploration. Journal of Learning Analytics, 3(1), 159–182.
Prinsloo, P., Slade, S. (2017). Big Data, Higher Education and Learning Analytics, Beyond Justice and Ethics of Care. B. Kei Daniel (ed.), Big Data and Learning Analytics in Higher Education, p. 109-124.
Rizvi, S., Rienties, B., Khoja, S. A. (2019). A role of demographics in online learning; A decision tree based approach. Computers & Education, 137, 32-47.
Selwyn, N. (2014). Distrusting educational technology. Critical questions for changing times. New York: Routledge.
Shibani, A., Knight, S., and Shum, S. B. (2019). “Contextualizable Learning Analytics Design: A Generic Model, and Writing Analytics Evaluations”. In Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19), p. 210-219.
Si Na, K., Tasir, Z. (2017). “A Systematic Review of Learning Analytics Intervention Contributing to Student Success in Online Learning”. In 2017 International Conference on Learning and Teaching in Computing and Engineering, p. 62-68.
Tsai,Y., Gasevic, D. (2017). “Learning Analytics in Higher Education – Challenges and Policies: A Review of Eight Learning Analytics Policies”. In Proceedings of the 7th International Conference on Learning Analytics & Knowledge (LAK’17), p. 233-242.
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.
Vieira, C., Parsons, P., Byrd, V. (2018). Visual learning analytics of educational data: A systematic literature review and research agenda. Computers & Education, 122, 119-135.
West. D., Huijser, H., Heath, D. (2016). Putting an ethical lens on learning analytics. Education Tech Research Dev, 64, 903–922.
Publicado
11/11/2019
Como Citar
MORAES, Marcia Cristina; FOLKESTAD, James E.; BIRMINGHAM, Daniel.
Critical Lens in Learning Analytics Research: A Systematic Literature Review. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 30. , 2019, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 1381-1390.
DOI: https://doi.org/10.5753/cbie.sbie.2019.1381.
