Mutlu Cukurova

An analysis framework for collaborative problem solving in practice-based learning activities: a mixed-method approach

Systematic investigation of the collaborative problem solving process in open-ended, hands-on, physical computing design tasks requires a framework that highlights the main process features, stages and actions that then can be used to provide meaningful learning analytics data. This paper presents an analysis framework that can be used to identify crucial aspects of the collaborative problem solving process in practice-based learning activities. We deployed a mixed-methods approach that allowed us to generate an analysis framework that is theoretically robust, and generalizable. Additionally, the framework is grounded in data and hence applicable to real-life learning contexts. This paper presents how our framework was developed and how it can be used to analyse data. We argue for the value of effective analysis frameworks in the generation and presentation of learning analytics for practice-based learning activities.

The NISPI framework: Analysing collaborative problem-solving from students' physical interactions

Collaborative problem-solving (CPS) is a fundamental skill for success in modern societies, and part of many common constructivist teaching approaches. However, its effective implementation and evaluation in both digital and physical learning environments are challenging for educators. This paper presents an original method for identifying differences in students CPS behaviours when they are taking part in face-to-face practice-based learning (PBL). The dataset is based on high school and university students hand position and head direction data, which can be automated deploying existing multimodal learning analytics systems. The framework uses Nonverbal Indexes of Students Physical Interactivity (NISPI) to interpret the key parameters of students CPS competence. The results show that the NISPI framework can be used to judge students CPS competence levels accurately based on their non-verbal behaviour data. The findings have significant implications for design, research and development of educational technology.

Supervised machine learning in multimodal learning analytics for estimating success in project-based learning

Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investig ...

Tracing physical movement during practice-based learning through multimodal learning analytics

In this paper, we pose the question, can the tracking and analysis of the physical movements of students and teachers within a Practice-Based Learning (PBL) environment reveal information about the learning process that is relevant and informative to Learning Analytics (LA) implementations? Using the example of trials conducted in the design of a LA system, we aim to show how the analysis of physical movement from a macro level can help to enrich our understanding of what is happening in the classroom. The results suggest that Multimodal Learning Analytics (MMLA) could be used to generate valuable information about the human factors of the collaborative learning process and we propose how this information could assist in the provision of relevant supports for small group work. More research is needed to confirm the initial findings with larger sample sizes and refine the data capture and analysis methodology to allow automation.

Machine and Human Observable Differences in Groups’ Collaborative Problem-Solving Behaviours

This paper contributes to our understanding of how to design learning analytics to capture and analyse collaborative problem-solving (CPS) in practice-based learning activities. Most research in learning analytics focuses on student interaction in digital learning environments, yet still most learning and teaching in schools occurs in physical environments. Investigation of student interaction in physical environments can be used to generate observable differences among students, which can then be used in the design and implementation of Learning Analytics. Here, we present several original methods for identifying such differences in groups CPS behaviours. Our data set is based on human observation, hand position (fiducial marker) and heads direction (face recognition) data from eighteen students working in six groups of three. The results show that the high competent CPS groups spend an equal distribution of time on their problem-solving and collaboration stages. Whereas, the low competent CPS groups spend most of their time in identifying knowledge and skill deficiencies only. Moreover, as machine observable data shows, high competent CPS groups present symmetrical contributions to the physical tasks and present high synchrony and individual accountability values. The findings have significant implications on the design and implementation of future learning analytics systems.

Diagnosing Collaboration in Practice-Based Learning: Equality and Intra-individual Variability of Physical Interactivity

Collaborative problem solving (CPS), as a teaching and learning approach, is considered to have the potential to improve some of the most important skills to prepare students for their future. CPS often differs in its nature, practice, and learning outcomes from other kinds of peer learning approaches, including peer tutoring and cooperation; and it is important to establish what identifies collaboration in problem-solving situations. The identification of indicators of collaboration is a challenging task. However, students physical interactivity can hold clues of such indicators. In this paper, we investigate two non-verbal indexes of student physical interactivity to interpret collaboration in practice-based learning environments: equality and intra-individual variability. Our data was generated from twelve groups of three Engineering students working on open-ended tasks using a learning analytics system. The results show that high collaboration groups have member students who present high and equal amounts of physical interactivity and low and equal amounts of intra-individual variability.

Revealing Behaviour Pattern Differences in Collaborative Problem Solving

The identification of effective Collaborative Problem Solving (CPS) strategies for practice based learning would make an important contribution to a better understanding of how to support the CPS process and how to design effective interventions. In this paper, we present a method for identifying effective CPS strategies using learner behaviours as the key to data to unpack this complex learning process. In order to distinguish learner behaviour patterns, we deployed an analysis framework for CPS that identifies fine-grained actions in practice-based learning activities. Then, using cumulative time plots we compared expert (those who have more experience in working together) behaviours with novice behaviours. Results show that participants with different levels of expertise in working together, present different behaviour patterns in collaborative problem solving.

A Digital Ecosystem for Digital Competences: The CRISS Project Demo

CRISS is a flexible and scalable cloud-based digital learning ecosystem that has the potential to allow the guided acquisition, evaluation and certification of digital competences. This demonstration will highlight some of the key activities under development, their underlying pedagogy and how the platform’s features support the acquisition, assessment and certification of digital competences.

Leveraging Non-cognitive Student Self-reports to Predict Learning Outcomes

Metacognitive competencies related to cognitive tasks have been shown to be a powerful predictor of learning. However, considerably less is known about the relationship between student’s metacognition related to non-cognitive dimensions, such as their affect or lifestyles, and academic performance. This paper presents a preliminary analysis of data gathered by Performance Learning Education (PL), with respect to students’ self-reports on non-cognitive dimensions as possible predictors of their academic outcomes. The results point to the predictive potential of such self-reports, to the importance of students exercising their self-understanding during learning, and to the potentially critical role of incorporating such student’s self-reports in learner modelling.

Interaction Analysis in Online Maths Human Tutoring: The Case of Third Space Learning

This ‘industry’ paper reports on the combined effort of researchers and industrial designers and developers to ground the automatic quality assurance of online maths human-to-human tutoring on best practices. We focus on the first step towards this goal. Our aim is to understand the largely under-researched field of online tutoring, to identify success factors in this context and to model best practice in online teaching. We report our research into best practice in online maths teaching and describe and discuss our design and evaluation iterations towards annotation software that can mark up human-to-human online teaching interactions with successful teaching interaction signifiers.