Analytics-enabled teaching as design

Alhadad, Sakinah S. J.; Thompson, Kate; Knight, Simon; Lewis, Melinda; Lodge, Jason M.

doi:10.1145/3170358.3170390

Cited by 10 publications

(7 citation statements)

References 41 publications

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“…As Alhadad, Thompson, Knight, Lewis, and Lodge (2018) argue, LA needs to move its focus away from "end-state outcomes" (p. 429) toward designs for student learning and teaching practice. However, education systems are continuously evaluated in terms of student outcome and value of investment, where student achievement needs to be measurable in some way.…”

Section: The Current Researchmentioning

confidence: 99%

Synergies of Learning Analytics and Learning Design: A Systematic Review of Student Outcomes

Blumenstein

2020

JLA

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The field of learning analytics (LA) has seen a gradual shift from purely data-driven approaches to more holistic views of improving student learning outcomes through data-informed learning design (LD). Despite the growing potential of LA in higher education (HE), the benefits are not yet convincing to the practitioner, in particular aspects of aligning LA data with LD toward desired learning outcomes. This review presents a systematic evaluation of effect sizes reported in 38 key studies in pursuit of effective LA approaches to measuring student learning gain for the enhancement of HE pedagogy and delivery. Large positive effects on student outcomes were found in LDs that fostered socio-collaborative and independent learning skills. Recent trends in personalization of learner feedback identified a need for the integration of student-idiosyncratic factors to improve the student experience and academic outcomes. Finally, key findings are developed into a new three-level framework, the LA Learning Gain Design (LALGD) model, to align meaningful data capture with pedagogical intentions and their learning outcomes. Suitable for various settings — face to face, blended, or fully online — the model contributes to data-informed learning and teaching pedagogies in HE.

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Section: The Current Researchmentioning

confidence: 99%

Synergies of Learning Analytics and Learning Design: A Systematic Review of Student Outcomes

Blumenstein

2020

JLA

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“…Although data literacy is an increasing focus of education research, LA raises new potential for the use of data in practice, yet little is understood in terms of how pre-service teachers at the start of their professional learning conceptualise data and its use in possible use in practice (Earl & Timperley, 2009). LA, as an enabling technology, requires the integration of skills in the areas of data literacy, learning design and the effective use of learning technologies (Alhadad et al, 2018;Thompson et al, 2018). To date, there is limited research on teachers' use of LA in schools (see, for example, the call for a special issue of the Journal for Learning Analytics; Society for Learning Analytics Research, 2020), and this work has not focused specifically on the data literacy of pre-service teachers.…”

Section: La and Learning Designmentioning

confidence: 99%

Data in practice: A participatory approach to understanding pre-service teachers’ perspectives

Prestigiacomo

Hunter

Knight

et al. 2020

AJET

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Data about learning can support teachers in their decision-making processes as they design tasks aimed at improving student educational outcomes. However, to achieve systemic impact, a deeper understanding of teachers’ perspectives on, and expectations for, data as evidence is required. It is critical to understand how teachers’ actions align with emerging learning analytics technologies, including the practices of pre-service teachers who are developing their perspectives on data use in classroom in their initial teacher education programme. This may lead to an integration gap in which technology and data literacy align poorly with expectations of the role of data and enabling technologies. This paper describes two participatory workshops that provide examples of the value of human-centred approaches to understand teachers’ perspectives on, and expectations for, data as evidence. These workshops focus on the design of pre-service teachers enrolled in teacher education programmes (N = 21) at two Australian universities. The approach points to the significance of (a) pre-service teachers’ intentions to track their students’ dispositions to learning and their ability to learn effectively, (b) the materiality of learning analytics as an enabling technology and (c) the alignment of learning analytics with learning design, including the human-centred, ethical and inclusive use of educational data in the teaching practice. Implications for practice or policy: Pre-service teachers ought to be given opportunities to engage and understand more about learning design, learning analytics and the use of data in classrooms. Professional experience placements for pre-service teachers should include participatory data sessions or learning design workshops. Teacher education academics in universities must be provided with ongoing professional development to support their preparation work of pre-service teachers’ data literacy, learning analytics and the increasing presence of data.

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“…Data literacy is essential for instructor interpretation and use of LA data. However, significant evidence suggests educators across all levels may not possess sufficient data literacy competencies (Alhadad et al, 2018;Hoogland et al, 2016;Ndukwe & Daniel, 2020;Tsai & Gašević, 2017). This is critical because difficulty analyzing and interpreting educational data hinders effective teacher inquiry (Sergis & Sampson, 2017).…”

Section: Data Literacymentioning

confidence: 99%

“…Farrell et al (2017) found the value university instructors placed on different types of LA related to their epistemic beliefs. Hence, researchers have observed that instructors practice epistemic fluency and data literacy when interpreting LA data and connecting it to their LD (Alhadad et al, 2018;Alhadad & Thompson, 2017).…”

Section: Pedagogical Lensmentioning

confidence: 99%

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Instructors' lived experiences of using learning analytics to improve online course design

Wood

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I am grateful to you. To Kelly, whose friendship has endured through it all. To Yang, Angie, Ka, Chris, EJ, Katherine, Jay, and others from Parkside, who have encouraged me, challenged me, and kept life interesting. It means so much to follow in Yang's Ed.D. footsteps. To Erica, whose kindness still touches me. To Katelynn, who held me accountable as a study buddy in Austin's coffee shops and bars.Thanks to my second family. To Kyle, for your curiosity and interest in my progress. Thanks to Kevin and Jessica for your welcoming spirit. Special acknowledgments to the countless hours Kevin and Kris dedicated to Apex Legends, Rumbleverse, and Rocket League while I was holed up writing. To Grace, for your positivity during the hard times and for raising three upstanding sons. Finally, all my love and gratitude to Kris. You came into my life right as I started this dissertation, pushed me through rough patches, and celebrated each milestone. I am a better human with a bigger heart because of you.

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Analytics-enabled teaching as design

Cited by 10 publications

References 41 publications

Synergies of Learning Analytics and Learning Design: A Systematic Review of Student Outcomes

Synergies of Learning Analytics and Learning Design: A Systematic Review of Student Outcomes

Data in practice: A participatory approach to understanding pre-service teachers’ perspectives

Instructors' lived experiences of using learning analytics to improve online course design

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