Designing for impact: a conceptual framework for learning analytics as self-assessment tools

Tormey, Roland; Hardebolle, Cécile; Pinto, Francisco; Jermann, Patrick

doi:10.1080/02602938.2019.1680952

Cited by 9 publications

(6 citation statements)

References 30 publications

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“…This study brings to light that the forced COVID‐19 situation creates opportunities for teachers and schools to rethink their vision on education and assessment. This could lead to more balanced assessment programs in education, in which FAs and summative assessments could be more aligned with a focus on learning (Cairns, 2020 ; Tormey et al, 2020 ; Wiliam, 2014b ). Teachers also intend to make use of their lessons learned and opportunities of online FA in their future blended FA practice, by combining opportunities of online FA with advantages of face‐to‐face FA to improve FA practice.…”

Section: Discussionmentioning

confidence: 99%

Secondary school teachers' use of online formative assessment during COVID‐19 lockdown: Experiences and lessons learned

Veugen

Gulikers

Brok

2022

Computer Assisted Learning

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Introduction During the COVID‐19 lockdown in 2020, teachers had to shift their teaching and assessment to online. Formative assessment (FA) can help teachers to engage, guide and monitor students' (online) learning. However, more knowledge is needed of how teachers could use the full FA process online. Methods In this study data from 50 secondary school teachers, who taught different grade levels and subjects and joined a FA learning network that started before and continued during the lockdown, were collected. The study investigates how they used online FA practices differently than face‐to‐face FA, what challenges and opportunities they experienced in online FA and what lessons they learned and intended to keep. This mixed methods study used data from a questionnaire, interviews and webinars that were segmented, coded and analysed. Results Results showed that many teachers implemented new FA strategies and adopted, more often than in their face‐to‐face practice, all the five phases of the FA process in an aligned matter in online FA. Teachers indicated opportunities in stimulating student engagement and guiding and monitoring student learning more at an individual level in the online FA process, but also experienced challenges, mainly in lack of interaction online. Discussion The sudden and necessary shift to online FA, due to the COVID‐19 lockdown, challenged teachers to more fundamentally reconsider their assessment practices and assumptions. Teachers intended to make use of these learned lessons to improve their future (blended) FA practice.

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Section: Discussionmentioning

confidence: 99%

Secondary school teachers' use of online formative assessment during COVID‐19 lockdown: Experiences and lessons learned

Veugen

Gulikers

Brok

2022

Computer Assisted Learning

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“…Urbina et al [10] listed the five most used models in the literature and their assessment strategies: Bandura's social-cognitive model [11], Boekaerts's Heuristic Model [12], Winne and Hadwin [13], Pintrich Model [9], and Zimmerman's cyclical model [8]. Self-regulated learning is generally understood as having a phased process with a forethought phase, a performance phase, and a self-reflection phase [14]. Intelligent self-regulation requires a clear definition of the objectives that are to be achieved.…”

Section: Self-regulated Academic Learningmentioning

confidence: 99%

Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study

Sobral

Oliveira

2021

BDCC

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Self-assessment is one of the strategies used in active teaching to engage students in the entire learning process, in the form of self-regulated academic learning. This study aims to assess the possibility of including self-evaluation in the student’s final grade, not just as a self-assessment that allows students to predict the grade obtained but also as something to weigh on the final grade. Two different curricular units are used, both from the first year of graduation, one from the international relations course (N = 29) and the other from the computer science and computer engineering courses (N = 50). Students were asked to self-assess at each of the two evaluation moments of each unit, after submitting their work/test and after knowing the correct answers. This study uses statistical analysis as well as a clustering algorithm (K-means) on the data to try to gain deeper knowledge and visual insights into the data and the patterns among them. It was verified that there are no differences between the obtained grade and the thought grade by gender and age variables, but a direct correlation was found between the thought grade averages and the grade level. The difference is less accentuated at the second moment of evaluation—which suggests that an improvement in the self-assessment skill occurs from the first to the second evaluation moment.

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Section: Introductionmentioning

confidence: 99%

“…Tormey et al (2020) highlight that even though learning analytics might support students' self‐assessment and related feedback, this might currently not be satisfactorily realized. To date, learning analytics do make not sufficiently use of assessment data (Ifenthaler et al, 2018), and the two lines of research are not adequately linked (Misiejuk & Wasson, 2017; Schumacher, 2020; Tormey et al, 2020). Hence, this exploratory case study aims to address this research gap by investigating students' engagement with self‐assessments using learning analytics approaches in a productive higher education learning environment.…”

Section: Introductionmentioning

confidence: 99%

Investigating students' use of self‐assessments in higher education using learning analytics

Schumacher

Kužílek

2022

Computer Assisted Learning

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Background: Formative assessments are vital for supporting learning and performance but are also considered to increase the workload of teachers. As selfassessments in higher education are increasingly facilitated via digital learning environments allowing to offer direct feedback and tracking students' digital learning behaviour these constraints might be reduced. Yet, learning analytics do not make sufficient use of data on assessments.Aims: This exploratory case study uses learning analytics methods for investigating students' engagement with self-assessments and how this relates to performance in the final exam and self-reported self-testing strategies. Materials & Methods:The research study has been conducted at a European university in a twelve-weeks course of a Bachelor's program in Economic and Business Education including n enroll = 159 participants. During the semester, students were offered nine self-assessments each including three to eight tasks plus one mid-term and one exam-preparation self-assessment including all prior self-assessments tasks.The self-assessment interaction data for each student included: the results of the last self-assessment attempt, the number of processed self-assessment tasks, and the time spent on the last self-assessment attempt, the total self-assessment attempts, and the first as well as last access of each self-assessment. Data analytics included unsupervised machine learning and process mining approaches.Results: Findings indicate that students use the self-assessments predominantly before summative assessments. Two distinct clusters based on engagement with self-assessments could be identified and engagement was positively related to performance in the final exam. The findings from learning analytics data were also indicated by students' self-reported use of self-testing strategies.Discussion: With the help of multiple data from self-reports, formal exams, and a learning analytics system, the findings provided multiple perspectives on the use of self-assessments and their relationships with course performance. These findings call Dirk Ifenthaler and Clara Schumacher contributed equally to this work and share the first authorship.

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Designing for impact: a conceptual framework for learning analytics as self-assessment tools

Cited by 9 publications

References 30 publications

Secondary school teachers' use of online formative assessment during COVID‐19 lockdown: Experiences and lessons learned

Secondary school teachers' use of online formative assessment during COVID‐19 lockdown: Experiences and lessons learned

Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study

Investigating students' use of self‐assessments in higher education using learning analytics

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