{"id":7,"date":"2022-11-20T20:46:54","date_gmt":"2022-11-20T20:46:54","guid":{"rendered":"http:\/\/cde22.education.ed.ac.uk\/mmannion\/?p=7"},"modified":"2022-11-20T20:46:54","modified_gmt":"2022-11-20T20:46:54","slug":"block-1-learning-with-data","status":"publish","type":"post","link":"http:\/\/cde22.education.ed.ac.uk\/mmannion\/2022\/11\/20\/block-1-learning-with-data\/","title":{"rendered":"Block 1: Learning With Data"},"content":{"rendered":"\n

Two of the major themes explored in this block that stood out to me include the datification of education as well as the presentation of \u2018personalised learning\u2019.<\/p>\n\n\n\n

The \u2018datafication of education\u2019 refers to the ways in which intensive data processing is increasingly pervading and reshaping formal educational activities (Knox, Williamson & Bayne 2019) Knox, Williamson and Bayne argue that the datafication of education has been largely influenced by Biesta\u2019s concept of \u2018Learnification\u2019 which places students or \u2018learners\u2019 at the forefront of these educational activities.<\/p>\n\n\n\n

Many data driven technologies have emerged in recent years that utilise information harvested from students or \u2018learners\u2019 to provide \u2018personalised\u2019 learning experiences in an attempt to confront the challenges presented by the diversity of individual preferences and the unique complexity of each human brain (Friesen, 2020, p.142)<\/p>\n\n\n\n

Personalised Learning systems take various forms that can be categorized in to either \u2018responsive\u2019 or adaptive learning systems.<\/p>\n\n\n\n

The level of \u2018personalisation\u2019 present in responsive systems can be as simple as allowing students to edit their profiles (Avatars, colour themes etc.) while more sophisticated responsive systems are data driven and can recommend learning materials appropriate to students level of proficiency through data collected from them. Some of the more advanced \u2018responsive\u2019 systems build on this data driven technology by using machine learning to adapt to students learning needs in real time. (Bulger, M. 2016. P. 6)<\/p>\n\n\n\n

One of the major issues that appears with the use of personalised learning systems is that a focus on data can lead to a \u2018laser focus\u2019 on numbers and other performance metrics that are quantifiable but fail to take in to account the whole learning process and social interactions that go along with it. (Bulger, M. 2016. P. 13). The social skills that students develop through classroom activities such as communication, collaborative skills, teamwork and relativity with ones peers in a particular learning environment are not at present tracked through personalised learning systems.<\/p>\n\n\n\n

The future of some personalized systems in development from IBM (Watson) and Carnegie Learning (Cognitive tutor) appears to take the form of an \u2018intelligent tutor\u2019 that aims to replicate human interaction by communicating conversationally and using facial recognition technologies to respond to human emotions displayed by the student.<\/p>\n\n\n\n

However, support for personalized or data driven learning systems is often grounded in intuitive or anecdotal  enthusiasms without a grounding in empirical evidence. (Bulger, M. 2016. P. 14)<\/p>\n\n\n\n

The idea of an intelligent tutor reminded me of a previous reading from the IDEL course last semester Where a student who had been having a conversation with a \u2018teacherbot\u2019 concluded that while they did not feel like they had learned anything, they had been prompted to think \u2013 and  \u201cIsn\u2019t this what every good teacher\/trainer strives for?\u201d , (Bayne S. (2015) p. 463). This view of learning and thinking being symbiotic underpins my own approach to and philosophy of teaching and is a key feature of Mike Hughes \u2018Magenta Principles\u2019 that has been very influential in recent years in the field of secondary education pedagogy  \u201clearning is the consequence of thinking\u2026 therefore our job is to get them to think\u201d<\/em> (Hughes, 2014)<\/p>\n\n\n\n

In Friesen\u2019s article, \u2018dialogue\u2019 is critiqued as the idealist view of learning personalization taking the form of one to one personal tutoring akin to something like Socrates and Plato, \u00a0Plato and Aristotle, and Aristotle and Alexander the Great resonated with me as a teacher of History and Classics. I began to reflect on all of the personal interactions I have with my own students through questioning (The so called \u2018Socratic method\u2019) \u2013 some of which would be quantifiable in data while others would probably not be so easy to \u2018Data-fy\u2019.<\/p>\n\n\n\n

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Learning analytics should leverage rather than replace human contact (Tsai, Perotta and Gasovic, 2019)  \u2013 Therefore  I made the human connection between teacher and students the focus of my visualisation<\/p>\n\n\n\n

Research shows that teachers can ask up to 400 questions per day and as many as 120 questions per hour (Levin & Long, 1981). I figured this may make the data difficult to record and so I decided to instead focus my data collection on the questions that the students of one of my smaller classes posed to me over a single week (four 40 minute lessons) This made the data easier to collect and also would better model how some learning personalization technologies function via recording of student\u2019s (learner\u2019s) data rather than the teacher.<\/p>\n\n\n\n

It proved to be a more challenging data collection than I thought! I began by ad hoc categorising the types of questions a particular group of students asked me throughout the course of a week\u2019s lessons on a sheet of paper in class and keeping a tally sheet for each student and making a tick beside each category when they asked me something.<\/p>\n\n\n\n

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Each student is represented by a cluster of coloured dots with each colour describing the type of question they asked me. I found I needed to create more categories as the types of questions they posed became more varied. Early attempts were made to also keep track of when certain students were able to answer one another\u2019s questions, and these are indicated by the lines connecting each student. I for sure missed a bunch of these moments throughout the week and in the course of the lessons and so this data is not as present in the visualization as the dots are.<\/p>\n\n\n\n

I also included a key to make the data easier to analyse. Students were assigned a number to keep them anonymous, and one student (#7) only has a small cluster as they were out sick for the majority of the week.<\/p>\n\n\n\n

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Bibliography<\/strong><\/p>\n\n\n\n

Bayne S. (2015). Teacherbot: interventions in automated teaching.<\/a> p. 463.<\/p>\n\n\n\n

Bulger, M. 2016. Personalized Learning: The Conversations We\u2019re Not Having. Data & Society working paper.<\/em><\/p>\n\n\n\n

Friesen, N. 2019. \u201cThe technological imaginary in education, or: Myth and enlightenment in \u2018Personalised Learning.<\/a>\u201d In M. Stocchetti (Ed.), The digital age and its discontents<\/em>. University of Helsinki Press.<\/p>\n\n\n\n

Levin, T, Long, R. 1981. Effective Instruction.<\/p>\n\n\n\n

Knox, J, Williamson, B & Bayne, S 2019, \u2018Machine behaviourism: Future visions of \u201clearnification\u201d and \u201cdatafication\u201d across humans and digital technologies<\/a>\u2018, Learning, Media and Technology<\/em>, 45(1), pp. 1-15.<\/p>\n","protected":false},"excerpt":{"rendered":"

Two of the major themes explored in this block that stood out to me include the datification of education as well as the presentation of \u2018personalised learning\u2019. The \u2018datafication of education\u2019 refers to the ways in which intensive data processing is increasingly pervading and reshaping formal educational activities (Knox, Williamson & Bayne 2019) Knox, Williamson […]<\/p>\n","protected":false},"author":7,"featured_media":12,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[5,3,4,6],"class_list":["post-7","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorised","tag-data-visualisation","tag-datafication","tag-learnification","tag-personalised-learning"],"_links":{"self":[{"href":"http:\/\/cde22.education.ed.ac.uk\/mmannion\/wp-json\/wp\/v2\/posts\/7","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/cde22.education.ed.ac.uk\/mmannion\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/cde22.education.ed.ac.uk\/mmannion\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/cde22.education.ed.ac.uk\/mmannion\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"http:\/\/cde22.education.ed.ac.uk\/mmannion\/wp-json\/wp\/v2\/comments?post=7"}],"version-history":[{"count":1,"href":"http:\/\/cde22.education.ed.ac.uk\/mmannion\/wp-json\/wp\/v2\/posts\/7\/revisions"}],"predecessor-version":[{"id":18,"href":"http:\/\/cde22.education.ed.ac.uk\/mmannion\/wp-json\/wp\/v2\/posts\/7\/revisions\/18"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/cde22.education.ed.ac.uk\/mmannion\/wp-json\/wp\/v2\/media\/12"}],"wp:attachment":[{"href":"http:\/\/cde22.education.ed.ac.uk\/mmannion\/wp-json\/wp\/v2\/media?parent=7"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/cde22.education.ed.ac.uk\/mmannion\/wp-json\/wp\/v2\/categories?post=7"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/cde22.education.ed.ac.uk\/mmannion\/wp-json\/wp\/v2\/tags?post=7"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}