SMILI☺: a Framework for Interfaces to Learning Data in Open Learner Models, Learning Analytics and Related Fields

Bull, Susan; Kay, Judy

doi:10.1007/s40593-015-0090-8

Cited by 109 publications

(67 citation statements)

References 60 publications

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“…The former can prompt the use of a specific aspect/rating while the latter can potentially show multiple perspectives critical for soft skill learning; (3) using open student models to aid students' awareness of their engagement together with open social student models [1,9,17] to allow social comparison to motivate participation (R2), foster meta-cognitive activities (R8), and indicate the quality of comments (R8 and R9).…”

Section: Discussion: Towards Intelligent Nudgingmentioning

confidence: 99%

Supporting Constructive Video-Based Learning: Requirements Elicitation from Exploratory Studies

Mitrović

Dimitrova

Lau

et al. 2017

Lecture Notes in Computer Science

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Abstract. Although videos are a highly popular digital medium for learning, video watching can be a passive activity and results in limited learning. This calls for interactive means to support engagement and active video watching. However, there is limited insight into what engagement challenges have to be overcome and what intelligent features are needed. This paper presents an empirical way to elicit requirements for innovative functionality to support constructive video-based learning. We present two user studies with an active video watching system instantiated for soft skill learning (pitch presentations). Based on the studies, we identify whether learning is happening and what kind of interaction contributes to learning, what difficulties participants face and how these can be overcome with additional intelligent support. Our findings show that participants who engaged in constructive learning have improved their conceptual understanding of presentation skills, while those who exhibited more passive ways of learning have not improved as much as constructive learners. Analysis of participants' profiles and experiences led to requirements for intelligent support with active video watching. Based on this, we propose intelligent nudging in the form of signposting and prompts to further promote constructive learning.

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Section: Discussion: Towards Intelligent Nudgingmentioning

confidence: 99%

Supporting Constructive Video-Based Learning: Requirements Elicitation from Exploratory Studies

Mitrović

Dimitrova

Lau

et al. 2017

Lecture Notes in Computer Science

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“…This section builds on the conceptual framework and definitions above to provide foundations for designing PUMLs. We draw on the SMILI☺ framework (Bull & Kay, , ) which described the purposes of OLMs: to support metacognitive processes of planning, monitoring and reflection; to facilitate collaboration or competition; for navigation; for assessment; improving the accuracy of the model; respecting the learner’s right to access and control their personal data, and to increase their trust in the model. …”

Section: Design Foundations: Competency Questions and Guidelinesmentioning

confidence: 99%

From data to personal user models for life‐long, life‐wide learners

Kay¹,

Kummerfeld²

2019

Brit J Educational Tech

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As technology has become ubiquitous in learning contexts, there has been an explosion in the amount of learning data. This creates opportunities to draw on the decades of learner modelling research from Artificial Intelligence in Education and more recent research on Personal Informatics. We use these bodies of research to introduce a conceptual model for a Personal User Model for Life‐long, Life‐wide Learners (PUMLs). We use this to define a core set of system competency questions. A successful PUML and its interface must enable a learner to answer these by scrutinising their PUML, aided by its scaffolding interfaces. We aim to give learners both control over their own learning data and the means to harness that data for the important metacognitive processes of self‐monitoring, reflection and planning. We conclude with a set of design guidelines for creating PUMLs. Our core contribution is a way to think about the design and evaluation of learning data and applications so that they give learner control and agency beyond simple data access and algorithmic transparency. What is already known about this topic There is decades of Artificial Intelligence in Education (AIED) research on learner modelling, personalisation and Open Learner Models (OLMs). There is a growing body of work on Personal Informatics. What this paper adds Drawing on the above research, we present a conceptual model showing how learning applications and data repositories relate to a Personal User Model for Life‐long, Life‐wide Learners (PUMLs). A set of competency questions to inform design and evaluation of PUMLs. Guidelines for designing interfaces that enable learners to scrutinise and control their learning data and models. Implications for practice and/or policy As universities create institutional repositories of learning data, our work takes a complementary, learner‐centred perspective of learning data, applications and repositories. PUMLs offer a mechanism to support student’s meta‐cognitive processes. PUMLs go beyond simplistic views of data access and transparency of algorithmic processes—empowering learners to scrutinise their long‐term data and its use.

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“…Assessment of learners has been grounded on different learning models, such as a SMILI framework, which provided the comparison between open learning models (OLM) (Bull & Kay, 2016). Architecture Extensible Problem-Specific Tutor (xPST) also empathizes on computer-based command and provides customized hints using the generation of the error message (Gilbert, Blessing, & Guo, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Move to Smart Learning Environment: Exploratory Research of Challenges in Computer Laboratory and Design Intelligent Virtual Laboratory for eLearning Technology

Munawar

Toor

Aslam

et al. 2018

EURASIA J MATH SCI T

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The university's computer laboratory is currently one of the most challenging aspects when imparting practical tasks with regards to the education technology (ET) enhancement. This study intends to observe the issues confronted by students while performing tasks in the laboratory in different educational modes. The online survey is conducted using quantitative and qualitative research instruments to evaluate the students' perspectives. This exploratory work has emphasized the practical issues such as an insufficient time constraint, and instruments, geographical needs, financial concerns, and unavailability of subject specialists to cater for relevant issues about a particular course. The sample size was (N= 161) drawn from a stratified sampling method for analysis of four strata. This research addresses these problems in the laboratory with an aim to improve the student's practical skills as well as their investigation-based learning. It is needed for practical based courses, through experimentation with the help of artificial intelligence (AI) paradigms. The design science methodology is adopted, it presents the conception of an Intelligent Virtual Laboratory (IVL) based on pedagogical agent-based cognitive architecture (PACA). This IVL provides the level of excellence of laboratory needs by enhancing the ET which students can efficiently perform practical tasks online at anywhere. The results showed that IVL has a significant model for enhancing the learning to students and recommendations for further research implementation.

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SMILI☺: a Framework for Interfaces to Learning Data in Open Learner Models, Learning Analytics and Related Fields

Cited by 109 publications

References 60 publications

Supporting Constructive Video-Based Learning: Requirements Elicitation from Exploratory Studies

Supporting Constructive Video-Based Learning: Requirements Elicitation from Exploratory Studies

From data to personal user models for life‐long, life‐wide learners

Move to Smart Learning Environment: Exploratory Research of Challenges in Computer Laboratory and Design Intelligent Virtual Laboratory for eLearning Technology

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