Measurement and its Uses in Learning Analytics

Bergner, Yoav

doi:10.18608/hla17.003

Cited by 27 publications

(22 citation statements)

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“…ALT could thus partly automate or support the teacher's tasks of mapping the pupils' activities, skill development and competencies and providing them with tasks and activities individually tailored to their needs. However, introducing learning analytics and adaptive learning into education has ethical and pedagogical implications (Bergner, 2017;Hoppe, 2017;Prinsloo & Slade, 2017). Winne (2017) points to the balance between accuracy and generalisation when describing a learner's ipsative development as a challenge, noticing that two learning signatures will never match completely: "The field of learning analytics will benefit from frequent consideration of this challenge" (Winne, 2017, p. 248).…”

Section: Learning Analytics and Adaptive Learning Technologymentioning

confidence: 99%

“…The fifth-grade pupils generally indicated that the program controlled their homework and learning processes in a way they did not appreciate. This could be seen in light of ethical and pedagogical implications of ALT and LA (Prinsloo & Slade, 2017;Bergner, 2017;Hoppe, 2017). The technology can give a precise overview of the pupils activities and thus be used to facilitate learning, but the overview can also be perceived as controlling by the pupils.…”

Section: Could You Describe a Typical Mathematics Class?mentioning

confidence: 99%

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Glimpses Into Real-Life Introduction of Adaptive Learning Technology: A Mixed Methods Research Approach to Personalised Pupil Learning

Moltudal

Høydal

Krumsvik

2020

Designs for Learning

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Section: Learning Analytics and Adaptive Learning Technologymentioning

confidence: 99%

Section: Could You Describe a Typical Mathematics Class?mentioning

confidence: 99%

Glimpses Into Real-Life Introduction of Adaptive Learning Technology: A Mixed Methods Research Approach to Personalised Pupil Learning

Moltudal

Høydal

Krumsvik

2020

Designs for Learning

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“…Although DKT has been reported to have more predicting accuracy in some cases, BKT is a more explanatory model (Pelánek 2017). Unlike DKT, the linkage of the BKT model to learning theory provides indepth and practical insights (explanatory power) for teachers to act on (Bodily et al 2018;Bergner 2017).…”

Section: Knowledge State Estimationmentioning

confidence: 99%

Including Learning Analytics in the Loop of Self-Paced Online Course Learning Design

Yan

Lin

Kinshuk

2020

Int J Artif Intell Educ

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Online education is growing because of its benefits and advantages that students enjoy. Educational technologies (e.g., learning analytics, student modelling, and intelligent tutoring systems) bring great potential to online education. Many online courses, particularly in self-paced online learning (SPOL), face some inherent barriers such as learning awareness and academic intervention. These barriers can affect the academic performance of online learners. Recently, learning analytics has been shown to have great potential in removing these barriers. However, it is challenging to achieve the full potential of learning analytics with the traditional online course learning design model. Thus, focusing on SPOL, this study proposes that learning analytics should be included in the course learning design loop to ensure data collection and pedagogical connection. We propose a novel learning design-analytics model in which course learning design and learning analytics can support each other to increase learning success. Based on the proposed model, a set of online course design strategies are recommended for online educators who wish to use learning analytics to mitigate the learning barriers in SPOL. These strategies and technologies are inspired by Jim Greer’s work on student modelling. By following these recommended design strategies, a computer science course is used as an example to show our initial practices of including learning analytics in the course learning design loop. Finally, future work on how to develop and evaluate learning analytics enabled learning systems is outlined.

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“…The evaluation of students' cognitive skills and non-cognitive traits and behaviors provide critical implications for designing student recruitment policies and personalizing tutorial instructions [Far03,Ber17,MHB19, JGP * 19, SQG * 19]. Cognitive skills refer to the core skills which the brain uses to think, read, learn, remember, reason, and pay attention, while noncognitive traits and behaviors refer to specific characteristics such as motivation, conscientiousness, perseverance, self-regulation, and collaboration [Far03].…”

Section: Introductionmentioning

confidence: 99%

“…Cognitive skills refer to the core skills which the brain uses to think, read, learn, remember, reason, and pay attention, while noncognitive traits and behaviors refer to specific characteristics such as motivation, conscientiousness, perseverance, self-regulation, and collaboration [Far03]. We can derive people's cognitive skills from their problem-solving outcomes [May92], which is why entrance exams and case tests are widely used in education and job recruitment processes [Ber17]. In contrast, noncognitive traits and behaviors (e.g., self-regulation), deeply valued in recruitment, are difficult to identify by an exam [Far03].…”

Section: Introductionmentioning

confidence: 99%

SeqDynamics: Visual Analytics for Evaluating Online Problem‐solving Dynamics

Xia

Lin

et al. 2020

Computer Graphics Forum

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Problem‐solving dynamics refers to the process of solving a series of problems over time, from which a student's cognitive skills and non‐cognitive traits and behaviors can be inferred. For example, we can derive a student's learning curve (an indicator of cognitive skill) from the changes in the difficulty level of problems solved, or derive a student's self‐regulation patterns (an example of non‐cognitive traits and behaviors) based on the problem‐solving frequency over time. Few studies provide an integrated overview of both aspects by unfolding the problem‐solving process. In this paper, we present a visual analytics system named SeqDynamics that evaluates students ‘problem‐solving dynamics from both cognitive and non‐cognitive perspectives. The system visualizes the chronological sequence of learners’ problem‐solving behavior through a set of novel visual designs and coordinated contextual views, enabling users to compare and evaluate problem‐solving dynamics on multiple scales. We present three scenarios to demonstrate the usefulness of SeqDynamics on a real‐world dataset which consists of thousands of problem‐solving traces. We also conduct five expert interviews to show that SeqDynamics enhances domain experts’ understanding of learning behavior sequences and assists them in completing evaluation tasks efficiently.

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Measurement and its Uses in Learning Analytics

Cited by 27 publications

References 0 publications

Glimpses Into Real-Life Introduction of Adaptive Learning Technology: A Mixed Methods Research Approach to Personalised Pupil Learning

Glimpses Into Real-Life Introduction of Adaptive Learning Technology: A Mixed Methods Research Approach to Personalised Pupil Learning

Including Learning Analytics in the Loop of Self-Paced Online Course Learning Design

SeqDynamics: Visual Analytics for Evaluating Online Problem‐solving Dynamics

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