Pub Date : 2018-10-03DOI: 10.4324/9781351113038-15
C. Gunn, J. McDonald
{"title":"Promoting learning analytics for tertiary teachers","authors":"C. Gunn, J. McDonald","doi":"10.4324/9781351113038-15","DOIUrl":"https://doi.org/10.4324/9781351113038-15","url":null,"abstract":"","PeriodicalId":265338,"journal":{"name":"Learning Analytics in the Classroom","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123629898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The perspective realism brings to learning analytics in the classroom","authors":"K. Bartimote, A. Pardo, P. Reimann","doi":"10.4324/9781351113038-3","DOIUrl":"https://doi.org/10.4324/9781351113038-3","url":null,"abstract":"","PeriodicalId":265338,"journal":{"name":"Learning Analytics in the Classroom","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116970798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Jovanović, D. Gašević, A. Pardo, Negin Mirriahi, S. Dawson
Citation for published version: Jovanovic, J, Pardo, A, Mirriahi, N, Dawson, S & Gasevic, D 2018, An analytics-based framework to support teaching and learning in a flipped classroom. in JM Lodge, J Cooney Horvath & L Corrin (eds), Learning Analytics in the Classroom: Translating Learning Analytics Research for Teachers. 1 edn, Routledge. https://doi.org/20.500.11820/b4b0b45d-72d9-4f39-929c-ea451288f253
已发表版本:Jovanovic, J, Pardo, A, Mirriahi, N, Dawson, S和Gasevic, D 2018，基于分析的框架，支持翻转课堂的教与学。在洛奇、霍瓦思、柯林主编的《课堂学习分析:教师学习分析研究的翻译》第1版，劳特利奇出版社。https://doi.org/20.500.11820/b4b0b45d-72d9-4f39-929c-ea451288f253
{"title":"An analytics-based framework to support teaching and learning in a flipped classroom","authors":"J. Jovanović, D. Gašević, A. Pardo, Negin Mirriahi, S. Dawson","doi":"10.4324/9781351113038-9","DOIUrl":"https://doi.org/10.4324/9781351113038-9","url":null,"abstract":"Citation for published version: Jovanovic, J, Pardo, A, Mirriahi, N, Dawson, S & Gasevic, D 2018, An analytics-based framework to support teaching and learning in a flipped classroom. in JM Lodge, J Cooney Horvath & L Corrin (eds), Learning Analytics in the Classroom: Translating Learning Analytics Research for Teachers. 1 edn, Routledge. https://doi.org/20.500.11820/b4b0b45d-72d9-4f39-929c-ea451288f253","PeriodicalId":265338,"journal":{"name":"Learning Analytics in the Classroom","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121677615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-03DOI: 10.4324/9781351113038-16
Deborah West, H. Huijser, D. Heath
This chapter draws on data collected as part of an Australian Office for Learning and Teaching (OLT) funded project, which explored the use of learning analytics for student retention and success (West et al., 2015). The focus in this chapter is on the critical role of leadership in learning analytics development and the challenges of implementation, especially when it comes to connecting learning analytics to the classroom. The challenges associated with learning analytics implementation are summarised as: 1) complexity of the learning experience; 2) coherence around accountability and role; 3) communication; 4) potential exclusion of teaching staff, and; 5) potential exclusion of the student voice. Strong and effective leadership has the potential to address these challenges to varying degrees. We argue in this chapter that successful development of learning analytics capability and capacity, as well as successful leveraging of such capacity, requires a combination of positional and distributive leadership, and it requires, above all, a coherent and consistent whole-of-institution approach. Most importantly however, applying learning analytics in a meaningful way that is applicable in the classroom requires people with different types of expertise and knowledge to work together, which in turn is significantly aided by strong and effective leadership.
{"title":"Blurring the boundaries","authors":"Deborah West, H. Huijser, D. Heath","doi":"10.4324/9781351113038-16","DOIUrl":"https://doi.org/10.4324/9781351113038-16","url":null,"abstract":"This chapter draws on data collected as part of an Australian Office for Learning and Teaching (OLT) funded project, which explored the use of learning analytics for student retention and success (West et al., 2015). The focus in this chapter is on the critical role of leadership in learning analytics development and the challenges of implementation, especially when it comes to connecting learning analytics to the classroom. The challenges associated with learning analytics implementation are summarised as: 1) complexity of the learning experience; 2) coherence around accountability and role; 3) communication; 4) potential exclusion of teaching staff, and; 5) potential exclusion of the student voice. Strong and effective leadership has the potential to address these challenges to varying degrees. We argue in this chapter that successful development of learning analytics capability and capacity, as well as successful leveraging of such capacity, requires a combination of positional and distributive leadership, and it requires, above all, a coherent and consistent whole-of-institution approach. Most importantly however, applying learning analytics in a meaningful way that is applicable in the classroom requires people with different types of expertise and knowledge to work together, which in turn is significantly aided by strong and effective leadership.","PeriodicalId":265338,"journal":{"name":"Learning Analytics in the Classroom","volume":"2082 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129844009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Building on an emerging body of literature on the use of learning analytics by educators, this chapter will explore the Open University (OU) approach to learning design that is currently being implemented on a large-scale. We aim to explain how this approach can be used to generate, visualise and interpret learning analytics that can be used by the educator to create learning design visualisations and data sets. Through comparison and analysis these can inform future design decisions. The basis for this OU approach can be found in the work of Conole (2012), whereby collaborative design teams use the Activity Type Classification Taxonomy to answer questions such as: What will students do in this module? How much will they be reading? What practical activities will they do? And, what does a ‘good’ learning design profile look like? The use of this taxonomy helps to establish a common language with which teachers can compare their teaching and learning practice with other teachers, schools and cluster groups. Where teachers are teaching from the same curriculum this data set, when visualised and analysed, can provide insight into the student learning experience. The approach allows for the measuring of what the student is doing, giving a different and unique view from those that only measure students by their academic ability. At the end of each section there is a short discussion entitled In the Classroom which suggests ways teachers in a classroom setting could adapt the approach for their circumstances.
{"title":"Gathering, visualising and interpreting learning design analytics to inform classroom practice and curriculum design","authors":"Tom Olney, B. Rienties, Lisette Toetenel","doi":"10.4324/9781351113038-6","DOIUrl":"https://doi.org/10.4324/9781351113038-6","url":null,"abstract":"Building on an emerging body of literature on the use of learning analytics by educators, this chapter will explore the Open University (OU) approach to learning design that is currently being implemented on a large-scale. We aim to explain how this approach can be used to generate, visualise and interpret learning analytics that can be used by the educator to create learning design visualisations and data sets. Through comparison and analysis these can inform future design decisions. The basis for this OU approach can be found in the work of Conole (2012), whereby collaborative design teams use the Activity Type Classification Taxonomy to answer questions such as: What will students do in this module? How much will they be reading? What practical activities will they do? And, what does a ‘good’ learning design profile look like? The use of this taxonomy helps to establish a common language with which teachers can compare their teaching and learning practice with other teachers, schools and cluster groups. Where teachers are teaching from the same curriculum this data set, when visualised and analysed, can provide insight into the student learning experience. The approach allows for the measuring of what the student is doing, giving a different and unique view from those that only measure students by their academic ability. At the end of each section there is a short discussion entitled In the Classroom which suggests ways teachers in a classroom setting could adapt the approach for their circumstances.","PeriodicalId":265338,"journal":{"name":"Learning Analytics in the Classroom","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123652864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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