{"id":2402,"date":"2022-06-30T15:55:21","date_gmt":"2022-06-30T14:55:21","guid":{"rendered":"https:\/\/blogs.manchester.ac.uk\/itl\/?p=2402"},"modified":"2022-06-30T16:19:01","modified_gmt":"2022-06-30T15:19:01","slug":"teacher-of-the-year-2021-humanities-an-evidence-based-approach","status":"publish","type":"post","link":"https:\/\/blogs.manchester.ac.uk\/itl\/2022\/06\/30\/teacher-of-the-year-2021-humanities-an-evidence-based-approach\/","title":{"rendered":"Teacher of the Year 2021 (Humanities): an evidence-based approach"},"content":{"rendered":"

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\"\"Dr R\u00e9ka Solymosi is a Senior Lecturer in Quantitative Methods within the School of Social Sciences, an Honorary Lecturer at University College London, and a member of the Software Sustainability Institute.\u00a0 She joined the University in 2016 after a period working as a data analyst with Transport for London.\u00a0 Her research focuses on making use of new forms of data to gain insight into people’s behaviour and subjective experiences, particularly focusing on crime, victimisation, transport and spatial research.\u00a0 R\u00e9ka was awarded Teacher of the Year 2021 for the Faculty of Humanities (one of the University’s annual Distinguished Achievement Awards).\u00a0\u00a0<\/strong><\/p>\n

In the first of this two-part feature on R\u00e9ka’s practice, she shares some practical tips she has found effective in her teaching of data literacy in social science study and practice.\u00a0<\/p>\n

An evidence-based approach\u00a0<\/h6>\n

Through my teaching I aim to promote an evidence-based approach, whether that be within my academic subject of data literacy or my own pedagogical practice. I find it’s helpful, both mentally and in practice, for me to have a firm foundation for the teaching I do. Setting aside time for attending conferences, talks and roundtables on teaching methods creates a kind of baseline peer support which informs how I teach. Learning from colleagues in the field who are passionate about promoting data literacy and quantitative analysis skills has been so important in improving my own knowledge and approach. When convening a course, I make a conscious effort to implement what I have learned.<\/p>\n

The key problem with teaching quantitative data and statistics to social science students, is that many of them feel like they \u2018didn\u2019t pick this topic – [they] wanted to do \u2018Maths\u2019\u2019<\/em>, it is understandable that when picking criminology, the Maths element of it isn’t the first thing on your list of related subjects. The students often want to split statistics away from their core subject area (i.e Criminology, Social Anthropology etc.) which of course doesn\u2019t work well. It\u2019s a recognized issue, with initiatives like the Q-Step Initiative<\/a> existing to promote data literacy in social science students.<\/p>\n

A 6-step approach to student engagement<\/h6>\n

So the question then is, how to engage a cohort of students who may begin the course already partially switched off? I begin by encouraging students to work hands-on with a project from day one, on a dataset that feels relevant to them. From there I guide them through a multifaceted teaching approach of my own creation, focusing on six main areas:<\/p>\n

    \n
  1. Required reading<\/strong>, done before the first lecture that week.<\/li>\n
  2. Pre-recorded lectures<\/strong> which consist of me guiding the students through specially constructed visuals to explain the week\u2019s concept.<\/li>\n
  3. Practical labs<\/strong>, where the students work through exercises with a lot of support and guidance.<\/li>\n
  4. A homework task<\/strong>, in the form of an A4 document with work for them to do and fill out answers on, based on the content of the practical lab.<\/li>\n
  5. Blackboard quizzes<\/strong>, which challenge the students by spot-checking the work they did in the homework task, and provides immediate feedback.<\/li>\n
  6. A discussion<\/strong> session – which is an optional and quite casual session focused around that week’s topic, where they can build upon what they learned.<\/li>\n<\/ol>\n

    There are a number of things that work particularly well with this approach. Firstly, with a cohort of 120+ students there simply isn’t the time to mark weekly progress evaluations. But by giving them a comprehensive evaluative task, and then spot-checking progress via Blackboard quiz with immediate feedback, students can stay abreast of their own progress, and identify gaps in understanding. If they are falling behind or finding a concept particularly hard, they can bring it to that week\u2019s discussion session to develop further. This approach means that throughout the week they are learning about a single topic across a range of learning styles.<\/p>\n

    \"\"<\/p>\n

    Fig 1: An example of the kind of interactions between students in the blackboard Threads<\/em><\/p>\n

    Tips on making time for students<\/h6>\n

    I think the key to being available for students is being extremely strict on yourself. I could happily spend 3 hours explaining one concept to a student! This can be avoided, however:<\/p>\n