Student Modeling: Supporting Personalized Instruction, from Problem Solving to Exploratory Open‐Ended Activities

Conati, Cristina; Kardan, Samad

doi:10.1609/aimag.v34i3.2483

Cited by 29 publications

(16 citation statements)

References 47 publications

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“…The pipeline first detects learners' behavioral clusters and then classifies a new learner to one of the predefined clusters, based on their logged actions. In Conati's and Kardan's () framework, the idea was to first relate the clusters' features to the learning outcomes and then isolate in each cluster those behaviors that are responsible for the learning effects. Next, as new users interact with the system, they are classified in real time into one of the clusters generated by the behavior discovery phase.…”

Section: Introductionmentioning

confidence: 99%

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Sharma¹,

Papamitsiou²,

Giannakos³

2019

Brit J Educational Tech

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Students' on‐task engagement during adaptive learning activities has a significant effect on their performance, and at the same time, how these activities influence students' behavior is reflected in their effort exertion. Capturing and explaining effortful (or effortless) behavior and aligning it with learning performance within contemporary adaptive learning environments, holds the promise to timely provide proactive and actionable feedback to students. Using sophisticated machine learning (ML) algorithms and rich learner data, facilitates inference‐making about several behavioral aspects (including effortful behavior) and about predicting learning performance, in any learning context. Researchers have been using ML methods in a “black‐box” approach, ie, as a tool where the input data is the learner data and the output is a given class from the chosen construct. This work proposes a methodological shift from the “black‐box” approach to a “grey‐box” approach that bridges the hypothesis/literature‐driven (feature extraction) “white‐box” approach with the computation/data‐driven (feature fusion) “black‐box” approach. This will allow us to utilize data features that are educationally and contextually meaningful. This paper aims to extend current methodological paradigms, and puts into practice the proposed approach in an adaptive self‐assessment case study taking advantage of new, cutting‐edge, interdisciplinary work on building pipelines for educational data, using innovative tools and techniques. What is already known about this topic Capturing and measuring learners' engagement and behavior using physiological data has been explored during the last years and exhibits great potential. Effortless behavioral patterns commonly exhibited by learners, such as “cheating,” “guessing” or “gaming the system” counterfeit the learning outcome. Multimodal data can accurately predict learning engagement, performance and processes. What this paper adds Generalizes a methodology for building machine learning pipelines for multimodal educational data, using a modularized approach, namely the “grey‐box” approach. Showcases that fusion of eye‐tracking, facial expressions and arousal data provide the best prediction of effort and performance in adaptive learning settings. Highlights the importance of fusing data from different channels to obtain the most suited combinations from the different multimodal data streams, to predict and explain effort and performance in terms of pervasiveness, mobility and ubiquity. Implications for practice and/or policy Learning analytics researchers shall be able to use an innovative methodological approach, namely the “grey‐box,” to build machine learning pipelines from multimodal data, taking advantage of artificial intelligence capabilities in any educational context. Learning design professionals shall have the opportunity to fuse specific features of the multimodal data to drive the interpretation of learning outcomes in terms of physiological learner states. The constraints from th...

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Section: Introductionmentioning

confidence: 99%

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Sharma¹,

Papamitsiou²,

Giannakos³

2019

Brit J Educational Tech

View full text Add to dashboard Cite

show abstract

“…Big data analyses, designed to detect and interpret variation are also essential as we analyze learning environments whose intrinsic mechanism and advertised virtue is divergence-variously named as adaptive or personalized learning (Conati & Kardan, 2013;Koedinger, Brunskill, Baker, & McLaughlin, 2013;McNamara & Graesser, 2012;Wolf, 2010). Standardized research interventions demand fidelity or strict uniformity of implementation.…”

Section: Opening a Window On Variationmentioning

confidence: 99%

Interpreting Evidence-of-Learning: Educational research in the era of big data

Cope¹,

Kalantzis²

2015

Open Review of Educational Research

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In this article, we argue that big data can offer new opportunities and roles for educational researchers. In the traditional model of evidence-gathering and interpretation in education, researchers are independent observers, who pre-emptively create instruments of measurement, and insert these into the educational process in specialized times and places (a pre-test or posttest, a survey, an interview, a focus group). The 'big data' approach is to collect data through practice-integrated research. If a record is kept of everything that happens, then it is possible analyze what happened, ex post facto. Data collection is embedded. It is on-the-fly and everpresent. With the relevant analysis and presentation software, the data is readable in the form of data reports, analytics dashboards and visualizations. We explore the methodological consequences of these developments for research methods.

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“…In both online and face-to-face contexts, we have also seen the introduction of interactive digital resources including games and simulations; classroom interactions via discussion feeds and forums that elicit more consistent and visible participation; recursive feedback systems which extend and in some cases transform traditional modes of formative and summative assessment DiCerbo & Behrens, 2014;Mislevy, Almond, & Lukas, 2004;Quellmalz & Pellegrino, 2009); and adaptive, personalized or differentiated instruction which calibrates learning to individual needs (Conati & Kardan, 2013;Shute & Zapata-Rivera, 2012;Walkington, 2013;Wolf, 2010). Such models and processes of instructional delivery are variously labeled 'constructivist', 'connectivist' or 'reflexive' (Kalantzis & Cope, 2012;Siemens, 2005).…”

Section: Sources Of Evidence-of-learning 197mentioning

confidence: 99%

Sources of Evidence-of-Learning: Learning and assessment in the era of big data

Cope¹,

Kalantzis²

2015

Open Review of Educational Research

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This article sets out to explore a shift in the sources of evidence-of-learning in the era of networked computing. One of the key features of recent developments has been popularly characterized as 'big data'. We begin by examining, in general terms, the frame of reference of contemporary debates on machine intelligence and the role of machines in supporting and extending human intelligence. We go on to explore three kinds of application of computers to the task of providing evidence-of-learning to students and teachers: (1) the mechanization of tests-for instance, computer adaptive testing, and automated essay grading; (2) data mining of unstructured data-for instance, the texts of student interaction with digital artifacts, textual interactions with each other, and body sensors;(3) the design and analysis of mechanisms for the collection and analysis of structured data embedded within the learning process-for instance, in learning management systems, intelligent tutors, and simulations. A consequence of each and all of these developments is the potential to record and analyze the 'big data' that is generated. The article presents both an optimistic view of what may be possible as these technologies and pedagogies evolve, while offering cautionary warnings about associated dangers.

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Student Modeling: Supporting Personalized Instruction, from Problem Solving to Exploratory Open‐Ended Activities

Cited by 29 publications

References 47 publications

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Interpreting Evidence-of-Learning: Educational research in the era of big data

Sources of Evidence-of-Learning: Learning and assessment in the era of big data

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