Workshops

Aim of the workshop

Using MOOCs as a case study, this workshop aims to provide an understanding of how Human-Computer Interaction (HCI) and Learning Analytics (LA) methods can be leveraged to design effective education technologies.

Objectives

• Provide an overview of methods used in HCI and Learning Analytics to build learner-centered education technologies
  
• Provide a background to related research ed tech and Learning Analytics
  

Description

Education Technologies based on Learner-centered designs are capable of focusing individuals and improving the learning effectiveness while scaling the number of learners. The effectiveness of such systems is based on not just the content, but also the interactions designed to cater to individuals with a smooth learning experience. A key component of such a system involves providing timely information to educational stakeholders (teachers, students, designers, administrators) to support better decision-making that uses LA and having an interface design that interacts with the stakeholders to operationalize such decisions and take actions that base on HCI. In this workshop, participants will understand how to leverage methods in Human-Computer Interactions and Learning Analytics in designing effective education technologies to serve learning at scale.

Target Audience

• Undergraduates/ postgraduate students who are interested in understanding HCI, LA methods and related research behind edtech
  
• Systems designers, UX/UI, learning experience designers for EdTech
  

Resource Person

Dr. Dilrukshi Gamage
Tokyo Institute of Technology, Japan
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Aim of the workshop

Providing the basic knowledge in handling the point cloud data for Deep learning to achieve 3D object detection and classification

Objectives

• To provide the basic knowledge in handling the point cloud data for Deep learning
  

Description

Point cloud data (PCD) can be applied for 3D object detection and classification using deep learning (DL). While treating the PCD to DL, down sampling, normalization, principal component analysis (PCA) and some other preprocessing are applied on PCD to reduce training time and enhance the features in the point cloud. This workshop mainly discusses those preprocessing methods and some applications of PCD to 3D object detection and classification using them.

Target Audience

Undergraduates and postgraduates

Resource Person

Prof. Chinthaka Premachandra
Shibaura Institute of Technology, Japan
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Aim of the workshop

This workshop aims to introduce students and researchers about the methods and resources used in the classification of hate content in social media.

Objectives

• Provide participants with hands-on experience in hate classification approaches.
• Allow the participants to understand the ongoing research related to hate classification.
• Encourage participants to share their experiences with potential limitations and caveats of hate classification and social media analytics in general.
• Explore the ethical and legal aspects of social media analytics

Description

Social media can be used as a tool for democratic and unrestricted expression. However, one of the clearest signs of a society lacking in democracy is the presence of hate speech in regular conversation. In hate speech, an individual or group is attacked because of their ethnicity, religion, sexual orientation, or gender. Due to people's increased reliance on social media for rapid information
dissemination, hate speech on it is a problem that is growing more and more common. Additionally, when compared to hate speech that occurs offline, the impact of hate speech online is greater. The rise of hate crimes in society is significantly impacted by the rise of hate speech on social media. In light of recent incidents in Sri Lanka and around the world, it will be beneficial to society if an accurate, effective approach to limiting the creation and dissemination of hate content on social media can be developed.
Using human-centred methods to identify hate content is impractical given the vast amount of social media information posted every second. Thus, to find hate content on social media, automated methods would be needed. The numerous methods used to collect data from social media, the annotation techniques used to categorize the data set into different hate categories and severity levels, and the classification models and methodologies will all be covered in this workshop.

Target Audience

This would serve as an introduction to the process of identifying hate content on social media. Students and researchers working in related fields would thus be the intended audience.

Resource Persons

Dr. Lochandaka Ranathunga,
Senior Lecturer,
Faculty of Information Technology, University of Moratuwa
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Dr. Supunmali Ahangama,
Senior Lecturer,
Faculty of Information Technology, University of Moratuwa
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Ms. Sandunika Hathnapitiya,
Research scholar,
Faculty of Information Technology, University of Moratuwa
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Ms. Suresha Perera,
Research scholar,
Faculty of Engineering, University of Moratuwa

Ms. Nirupama Rajapaksha,
Research scholar,
Faculty of Information Technology, University of Moratuwa
and NLP Data Scientist, NimbusMaps
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Mr. Maneesha Caldera,
Research scholar,
Faculty of Engineering, University of Moratuwa
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