The review of educational robotics research and the need for real-world interaction analysis

Hong, Nicholas Wong Wai; Chew, Esyin; Sze-Meng, Jojo Wong

doi:10.1109/icarcv.2016.7838707

Cited by 12 publications

(9 citation statements)

References 33 publications

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“…LA research focuses on children’s actions while they are interacting with either game‐based learning or learning‐management systems (Agus & Samuri, 2018; Ochoa & Worsley, 2016) in order to assess the child’s performance quantitatively, usually using a desktop computer and giving them personalised feedback. However, according to Hong, Chew and Sze‐Meng (2016), this approach is of limited use in providing reliable information about the student’s motivation or engagement with a task: in other words, these data do not provide evidence of “meaningful learning experiences.” Moreover, Giannakos and colleagues found that “traditional click‐stream models achieve a 39% error rate in predicting learning performance […] while for fused multimodal the error drops up to 6%” (2019, p. 108). For this reason, in the case of MMLA the object of study extends to learners’ behavioural reactions while interacting with a device or carrying out a task, including their emotional, cognitive and physiological responses.…”

Section: Introductionmentioning

confidence: 99%

“…Hong et al . (2016) had already delved into the “assessment and feedback” objective by conducting a review of 62 references between 2008 and 2016 on the use of educational robotics research with children to assess student outcomes in educational research. As in the case of Agus and Samuri (2018), they concluded that LA is employed mainly through Learning Management Systems.…”

Section: Introductionmentioning

confidence: 99%

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Multimodal Learning Analytics research with young children: A systematic review

Lanna¹

2020

Brit J Educational Tech

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Learning Analytics and Multimodal Learning Analytics are changing the way of analysing the learning process while students interact with an educational content. This paper presents a systematic literature review aimed at describing practices in recent Multimodal Learning Analytics and Learning Analytics research literature in order to identify tools and strategies useful for the assessment of the progress and behaviour of children under 6 years old in respect of their learning. The purpose is to provide guidance for Multimodal Learning Analytics research with children under 6 years old to assess their engagement in a task, their emotions, attention, understanding and achievement of a goal. The current state of knowledge on Multimodal Learning Analytics research suggests how performance analytics, face and speech recognition systems, eye tracking, Kinect analytics and wristbands could be used with children. The results show the complexity of collecting data using non‐invasive methodologies with children under 6 years old. Ethical implications related to multimodal data from audio, visual, biometric and quantitative measures of child behaviour are discussed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multimodal Learning Analytics research with young children: A systematic review

Lanna¹

2020

Brit J Educational Tech

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“…Thus, when creating an activity we have to define a score parameter for each skill that represents the level of knowledge that the skill contributes to (W i ). 5 Table 2 shows some of the activities defined in IDEE in two different experiences (inclined plane and uniform rectilinear movement), the skills involved and their corresponding scores.…”

Section: Students' Knowledge Tracing In Ideementioning

confidence: 99%

Supporting Teachers to Monitor Student’s Learning Progress in an Educational Environment With Robotics Activities

2020

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Educational robotics has proven its positive impact on the performances and attitudes of students. However, the educational environments that employ them rarely provide teachers with relevant information that can be used to make an effective monitoring of the student learning progress. To overcome these limitations, in this paper we present IDEE (Integrated Didactic Educational Environment), an educational environment for physics, that uses EV3 LEGO Mindstorms R educational kit as robotic component. To provide support to teachers, IDEE includes a dashboard that provides them with information about the students' learning process. This analysis is done by means of an Additive Factor Model (AFM). That is a well-known technique in the educational data mining research area. However, it has been usually employed to carry out analysis about students' performance data outside the system. This can be a burden for the teacher who, in most cases, is not an expert in data analysis. Our goal in this paper is to show how the coefficients of AFM provide valuable information to the teacher without requiring any deep expertise in data analysis. In addition, we show an improved version of the AFM that provides a deeper understanding about the students' learning process.

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“…Hence, it is time to focus on ER’s teaching-learning processes, methods, and approaches used in the pre-university education context, detect which are useful to teach and learn STEM concepts and skills and if the convergence of educational data analysis [ 84 ] as support approaches, such as Learning Analytics [ 85 , 86 ], can help to improve learning ER [ 87 , 88 , 89 ].…”

Section: Introductionmentioning

confidence: 99%

Systematic Review on Which Analytics and Learning Methodologies Are Applied in Primary and Secondary Education in the Learning of Robotics Sensors

Fonseca

Fox

Fonseca

et al. 2020

Sensors

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Robotics technology has become increasingly common both for businesses and for private citizens. Primary and secondary schools, as a mirror of societal evolution, have increasingly integrated science, technology, engineering and math concepts into their curricula. Our research questions are: “In teaching robotics to primary and secondary school students, which pedagogical-methodological interventions result in better understanding and knowledge in the use of sensors in educational robotics?”, and “In teaching robotics to primary and secondary school students, which analytical methods related to Learning Analytics processes are proposed to analyze and reflect on students’ behavior in their learning of concepts and skills of sensors in educational robotics?”. To answer these questions, we have carried out a systematic review of the literature in the Web of Science and Scopus databases regarding robotics sensors in primary and secondary education, and Learning Analytics processes. We applied PRISMA methodology and reviewed a total of 24 articles. The results show a consensus about the use of the Learning by Doing and Project-Based Learning methodologies, including their different variations, as the most common methodology for achieving optimal engagement, motivation and performance in students’ learning. Finally, future lines of research are identified from this study.

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The review of educational robotics research and the need for real-world interaction analysis

Cited by 12 publications

References 33 publications

Multimodal Learning Analytics research with young children: A systematic review

Multimodal Learning Analytics research with young children: A systematic review

Supporting Teachers to Monitor Student’s Learning Progress in an Educational Environment With Robotics Activities

Systematic Review on Which Analytics and Learning Methodologies Are Applied in Primary and Secondary Education in the Learning of Robotics Sensors

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