Student Awareness Model based on student affective response and generic profiles

Shminan, Ahmad Sofian; Tamura, Toshihiro; Huang, Runhe

doi:10.1109/icist.2012.6221731

Cited by 3 publications

(5 citation statements)

References 22 publications

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“…increase fidelity of implementation, by parents, of evidence-based practices). Future studies could also adopt personalized learning approaches that can adapt the learning material to the needs of the individual learner [26], [27]. This could further save time and make the learning experience more motivating.…”

Section: Discussionmentioning

confidence: 97%

The intensity of the research activities on e learning for care givers of autistic children

Shminan

Fauzan

Aren

2015

2015 International Conference on Information Technology Systems and Innovation (ICITSI)

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Autism, known as a "spectrum disorder" (ASD) is seen in early childhood or by three years of age. ASD is a neurodevelopmental disorder characterized by deficits in social responsiveness, impairments in verbal and nonverbal communication. The purpose of this paper is to demonstrate the relevance of e-learning technology to the area of training the caregivers of autistic children. Our search focused on a number of data banks that contain numerous references to autism and home based treatment. Our results found only ten papers published since 2010 that met our criteria for inclusion. Six were demonstrations of e learning to teach caregivers and professionals the basics of applied behavior analysis and some techniques for skill training. The remainder of the studies fell into the telehealth category which involved direct communication between a professional and caregiver in the home. The studies suggest that home based service delivery is effective and offers both the social service system and parents considerable financial savings. The use of Web based platform (e learning and telehealth) is depicted as an aid to caregivers of autistic children. The need for more studies of the variables related to home based service delivery is noted.

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Section: Discussionmentioning

confidence: 97%

The intensity of the research activities on e learning for care givers of autistic children

Shminan

Fauzan

Aren

2015

2015 International Conference on Information Technology Systems and Innovation (ICITSI)

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“…This article follows on from our first study [2][4] which focused on the identification of the main characteristics of students influencing student motivation and patterns of learning. This article concentrates on the formation process of dynamical student profiling, stressing the system of analyzing data.…”

Section: Discussionmentioning

confidence: 99%

“…Four prototype applications were developed, namely a student dynamic behaviour recognition application, a student learning pattern information extraction application, a decision tree application, and classroom simulation application. Two applications were crucial to support data collection processes and clarify the data structure image to the researcher for simulation development [2]. Meanwhile, the development of the id3 classifier serves to extract the specific rules for personalized teaching decision making strategies.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…To accomplish this, a teaching strategy needs to fit every student, where comprehensive profile information on the student chronicles their behavior and traits [2]. In effect, any attempt to resolve different learning settings demands requires different mechanisms with varying emphases.…”

Section: Introductionmentioning

confidence: 99%

“…This requires two elements of primary information, namely data on students' affective responses and generic profile data. The former relies on data on the body language of students obtained through sensory devices used in tandem with Microsoft Kinect, while the latter is obtained through a survey instrument based on the MBTI theory extracted through the previous assessment device [2]. The combination of both types of information is later analyzed using decision tree algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic student assessment to advocate personalized learning plan

Shminan

Othman

2015

2015 International Conference on Information Technology Systems and Innovation (ICITSI)

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A central challenge in education is to match instruction to the characteristics and learning styles of students in order to optimize learning. In this article, we intend to outline our approach to supporting personalized learning strategies by constructing dynamical student profiling using ubiquitous computing capability. This profiling includes recorded data on students' affective responses to learning to discern students' level of motivation and details from generic student profiles to describe and predict student learning patterns. Learning pattern data analysis derives conclusions using decision trees. Through this process, information can be extracted from students' affective responses and students' profile data and relevant correlations between the two data sets can be recognized automatically. A personalized learning component uses this information to offer proactive support to students. This is achieved by recommending personalized courses of action which are beneficial to students. Our proposed model has been tested in a classroom simulation. Issues of sample limitations and promising directions for future research are elaborated towards the end of this paper.

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Classifying a Person’s Degree of Accessibility From Natural Body Language During Social Human–Robot Interactions

McColl

Jiang

Nejat

2017

IEEE Trans. Cybern.

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For social robots to be successfully integrated and accepted within society, they need to be able to interpret human social cues that are displayed through natural modes of communication. In particular, a key challenge in the design of social robots is developing the robot's ability to recognize a person's affective states (emotions, moods, and attitudes) in order to respond appropriately during social human-robot interactions (HRIs). In this paper, we present and discuss social HRI experiments we have conducted to investigate the development of an accessibility-aware social robot able to autonomously determine a person's degree of accessibility (rapport, openness) toward the robot based on the person's natural static body language. In particular, we present two one-on-one HRI experiments to: 1) determine the performance of our automated system in being able to recognize and classify a person's accessibility levels and 2) investigate how people interact with an accessibility-aware robot which determines its own behaviors based on a person's speech and accessibility levels.

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Student Awareness Model based on student affective response and generic profiles

Cited by 3 publications

References 22 publications

The intensity of the research activities on e learning for care givers of autistic children

The intensity of the research activities on e learning for care givers of autistic children

Dynamic student assessment to advocate personalized learning plan

Classifying a Person’s Degree of Accessibility From Natural Body Language During Social Human–Robot Interactions

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