eprintid: 10142299 rev_number: 23 eprint_status: archive userid: 608 dir: disk0/10/14/22/99 datestamp: 2022-03-03 15:37:33 lastmod: 2024-09-01 06:10:05 status_changed: 2022-03-03 15:37:33 type: book_section metadata_visibility: show creators_name: Littlejohn, A creators_name: Kennedy, E creators_name: Laurillard, D title: Professional Learning Analytics: Understanding complex learning processes through measurement, collection, analysis, and reporting of MOOC data ispublished: pub divisions: UCL divisions: B16 divisions: B14 divisions: J77 keywords: Professional learning, Learning analytics, Online learning, Digital education, Digital data, Value creation note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: Global, organisational and technological changes are transforming the world of work. To respond to these changes, professionals need to be able to adapt and upskill flexibly, elevating the need for lifelong professional learning. Technological systems are being used to provide professional learning at scale, supported by learning analytics to scaffold the learner. Learning Analytics has been defined as ‘the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs’ (Siemens G, Long P, EDUCAUSE Rev 46(5), 2011, p. 30). It comprises a range of methods, from ‘predictive analytics’ that identify ‘at risk’ learners, to ‘multimodal’ methods that bring together diverse data to help inform learners. Alongside potential benefits of Learning Analytics are a number of problems of datafication, such as increased workplace surveillance, automation of decisions and making use of available data, rather than identifying specific data needed to answer research questions. This chapter considers these and other methodological challenges, through presentation of case examples of forms of Learning Analytics from professional development Massive Open Online Courses (MOOCs). Each example considers the complex processes within professional learning and how these might be analysed in future using new methods for Professional Learning Analytics. date: 2022-08-31 date_type: published publisher: Springer Nature official_url: https://doi.org/10.1007/978-3-031-08518-5_25 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1915802 doi: 10.1007/978-3-031-08518-5_25 isbn_13: 978-3-031-08517-8 lyricists_name: Kennedy, Eileen lyricists_name: Laurillard, Diana lyricists_name: Littlejohn, Allison lyricists_id: ETKEN55 lyricists_id: DMLAU06 lyricists_id: ALITT35 actors_name: Littlejohn, Allison actors_id: ALITT35 actors_role: owner full_text_status: public series: Professional and Practice-based Learning (PPBL) volume: 33 place_of_pub: Cham, Switzerland pagerange: 557-578 book_title: Methods for Researching Professional Learning and Development. Professional and Practice-based Learning editors_name: Goller, M editors_name: Kyndt, E editors_name: Paloniemi, S editors_name: Damsa, C citation: Littlejohn, A; Kennedy, E; Laurillard, D; (2022) Professional Learning Analytics: Understanding complex learning processes through measurement, collection, analysis, and reporting of MOOC data. In: Goller, M and Kyndt, E and Paloniemi, S and Damsa, C, (eds.) Methods for Researching Professional Learning and Development. Professional and Practice-based Learning. (pp. 557-578). Springer Nature: Cham, Switzerland. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10142299/1/littlejohn-professionallearninganalytics-maindocument-ACCEPTED%20-200122.pdf