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H817 – 20b – Week 24 – Activity 18 Part 2 – Report

H817 – 20b – Week 24 – Activity 18 Part 2 – Report

Introduction This short report will attempt to highlight the changes required in our institution to achieve the management’s ambition of supporting learning and teaching by the use of learning analytics. The field of learning analytics is a complex and rapidly evolving one. This report will hopefully provide some clarity on the current developments in learning analytics and how they could successfully be applied in our context. The definition of Learning Analytics suggested by Siemens (2010) has been stable on Wikipedia…

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H817 – 20b – Week 24 – Activity 17 – Why analytics may be ignored

H817 – 20b – Week 24 – Activity 17 – Why analytics may be ignored

* Dawson and Macfadyen group the reasons for lack of uptake under two headings: ‘Perceived attributes of an innovation’ and ‘The realities of university culture’. In a blog post, or in your learning journal, note the reasons they identify for lack of uptake, and choose your own headings to group them under. In the Dawson et al. (2012) paper numerous reasons for the lack of uptake of the findings of data studies are given. The reasons for this are listed…

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H817 – 20b – Week 23 – Activity 16 – Socialised learning analytics

H817 – 20b – Week 23 – Activity 16 – Socialised learning analytics

Return to the article that you began reading in Activity 15: Ferguson and Buckingham Shum (2012), Social learning analytics: five approaches. Finish reading the article and make notes on the different types of socialised learning analytic and how they might be implemented. Social learning network analytics The Ferguson et al. (2012) paper notes that Social Learning Analytics (SLAs) may be implemented using the (now defunct) SNAPP tool (Social Networks Adapting Pedagogical Practice). This tool produces visualisations of forum contributions to…

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H817 – 20b – Week 23 Activity 15 – Citation networks

H817 – 20b – Week 23 Activity 15 – Citation networks

* Note what the authors aimed to do in the paper, and what their main findings were. The authors Dawson et al. (2014) aimed to discover a framework of sorts to make sense of the many methodologies and approaches to studying learning analytics. They found divergence in the computing and education approaches to analytics research as reflected in the published works form the LAK conferences. The abstract also highlights the conceptual nature of the commonly references research works. * Now…

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H817 – 20b – Week 23 – Activity 14 Activity 14 Part 2: Visualising social networks

H817 – 20b – Week 23 – Activity 14 Activity 14 Part 2: Visualising social networks

Try creating your own social network diagram. Take a recent thread in the tutor group forum, which includes six or more postings, and sketch it as a network diagram. Note who appears to be central to the discussion. It’s quite frustrating that the SNAPP project is defunct so there seems to be no opportunity to actually acquire the tool. I can’t find a code repository but there may be one somewhere. The best I could do was get a waybackmachine…

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H817 – 20b – Block 4 – Week 23 Activity 14 – Network Diagrams – PART 1

H817 – 20b – Block 4 – Week 23 Activity 14 – Network Diagrams – PART 1

Read this short paper that provides an introduction to social network visualisations and to the SNAPP tool: Bakharia et al. (2009), Social networks adapting pedagogical practice: SNAPP. In your learning journal, or blog, make a note of the things that can be revealed by a network diagram of students’ discussions. The authors identify six – you may be able to think of more. Use these as headings and, under each one, note how this information could be used to support…

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H817 – 20b – Block 4 Activity 13 – Social Analytics

H817 – 20b – Block 4 Activity 13 – Social Analytics

* Prepare slides for a ten-minute presentation that can be used to explain to staff what social learning analytics are, why they are becoming more important and the benefits they may offer. Use suitable software, such as PowerPoint, Keynote, Prezi or something else you know well. [ngg_images source=”galleries” container_ids=”2″ display_type=”photocrati-nextgen_basic_thumbnails” override_thumbnail_settings=”1″ thumbnail_width=”120″ thumbnail_height=”80″ thumbnail_crop=”1″ images_per_page=”0″ number_of_columns=”4″ ajax_pagination=”0″ show_all_in_lightbox=”0″ use_imagebrowser_effect=”0″ show_slideshow_link=”1″ slideshow_link_text=”[Show slideshow]” order_by=”sortorder” order_direction=”ASC” returns=”included” maximum_entity_count=”500″]My target audience for this presentation would be lecturing and professional development staff in my…

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H817 – 20b – Week 22 – Activity 12 – Checkpoint and Process Analytics

H817 – 20b – Week 22 – Activity 12 – Checkpoint and Process Analytics

Look through the learning outcomes and learning activities in the block, noting where/why checkpoint analytics might be used and where/why process analytics might be used. You will be writing from an informed perspective, with an eye to what you would have found useful as a learner. In looking back at Block 2 I realise that the entire course is designed in a social constructivist way. The ‘average’ activity in this block involves reading the resources provided then taking part in…

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H817 – 20b – Week 22 – Activity 11 – LA and LD

H817 – 20b – Week 22 – Activity 11 – LA and LD

Make notes in your learning journal or blog about these types of analytic and when they can be applied. Consider whether these classifications are more useful than the ones you have considered at other points this week. Checkpoint After studying Lockyer et al. (2013) I found that checkpoint analytics indicate that a student has met some prerequisite such as downloading a resource or viewing a Moodle book. This relates to the resources in learning design. Checkpoint analytics can be used…

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H817 – 20b – Week 22 – Activity 10 – Library

H817 – 20b – Week 22 – Activity 10 – Library

Make a list of the types of data collected in university libraries. These range from swipe card data about student visitors to usage figures for specific websites. As outlined in Library Impact Data Project (LIDP) (2012a) Demographics of students (mature/non-mature students) Gender of users Ethnicity Country of Domicile As outlined in LIDP (2012b) Subject studied by user (Science, Health, Arts) Number of visits Number of PDF Downloads Hours logged in to eResources As listed in LIDP (2012c) Overnight usage Number…

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