My First Phd Seminar presentation Template

1. A DEEP LEARNING-BASED STUDENTS ENGAGEMENT DETECTION AND ENHANCEMENT MODEL
IN E-LEARNING SYSTEM TO IMPROVE THEIR COGNITIVE SKILLS
Presented By: ALI AIJAZ SHAR
Registration # COM19-19
SUPERVISOR PROF.DR. SAMINA RAJPER
Professor, Institute of Computer Science
CO-SUPERVISOR PROF. DR NOOR AHMED SHAIKH
Professor, Institute of Computer Science
INSTITUTE OF COMPUTER SCIENCE
FACULTY OF PHYSICAL SCIENCE
SHAH ABDUL LATIF UNIVERSITY, KHAIRPUR
2023
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2. Agenda
Introduction
Problem Statement
Research Objectives
Methodology
Dataset
Dataset Implementation 2

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4.  Student engagement is a critical factor in e-learning, as it directly impacts learning outcomes,
student satisfaction, and retention. Engaged students are more likely to pay attention,
participate in class activities, and complete their assignments. They are also more likely to
develop a deeper understanding of the material and retain information better over time.
Introduction
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5. • A deep learning-based student engagement detection and enhancement model can be used
to detect students who are at risk of disengagement and provide them with timely
interventions to help them re-engage. The model can also be used to identify students who are
struggling with certain concepts and provide them with personalized support.
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6. Enhancing Student Engagement in E-Learning
Enhancing Student Engagement in E-Learning
Key Factor Impact on E-Learning
Student Engagement Enhancement • Directly impacts learning outcomes,
satisfaction, and retention rates
• Engaged students are more likely to: - Pay
attention in class
• Participate actively
• Complete assignments
• Develop deeper understanding of material
and retain information better over time
Deep Learning-Based Student Engagement Model • Detect students at risk of disengagement
Student Engagement Model
• Provide timely interventions for re-
engagement
• - Identify students struggling with specific
concepts and offer personalized support

7. Background of study
E-learning systems have become increasingly popular in recent years, as they offer several
advantages over traditional face-to-face learning. E-learning systems are more flexible and
convenient, and they can be accessed from anywhere with an internet connection. However, e-
learning systems also present some challenges. One of the biggest challenges is maintaining
student engagement.
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8. Statement of problem
The advent of digitization in education has brought changes to the education system, but also presents challenges for teachers in
determining student engagement and understanding. The problem of student disengagement is a growing concern and can lead to
low achievement. One solution proposed is the use of a laptop's built-in webcam to track students' eye and facial movements in
real-time using algorithms like The Haar-cascade algorithm is a feature-based object detection method that uses Haar-like features
to detect objects in an image. The problem addressed in the topic "A Deep Learning-based Students Engagement Detection Model
in E-Learning System to Improve their Cognitive Skills" is the inadequacy of current e-learning systems in accurately detecting
student engagement, which has been identified as a crucial factor in enhancing their cognitive skills
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9. Despite the increasing use of e-learning systems, current methods for detecting student engagement are limited and
often rely on self-reported measures . This hinders the ability to provide personalized feedback and support to students
in real-time, leading to decreased engagement and ineffective learning (Bang et al., 2019). To address the issue, a
solution is being proposed which involves the creation of a model based on deep learning techniques. The objective of
this model is to accurately assess the level of engagement of students and provide instantaneous feedback. This
approach aims to improve the learning experience and enhance cognitive skills.
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10. Research Hypothesis
H0: The implementation of a deep learning-based student engagement detection model
in an e-learning system will NOT significantly improve students' cognitive skills
compared to traditional engagement detection methods”.
H1: "The implementation of a deep learning-based student engagement detection model
in an e-learning system will significantly improve students' cognitive skills compared to
traditional engagement detection methods”.
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The primary objectives of this research are:
To Develop a Novel Deep Learning-Based Model for detecting student engagement in e-learning systems.
To Evaluate the accuracy and effectiveness of the model in detecting student engagement in real-time.
To Assess the impact of the model on improving students' cognitive skills, such as retention and comprehension
of information.
To Explore the limitations and challenges of the model and suggest areas for future improvement.

12. Research Questions
 How can a deep learning-based model be used to detect and enhance student engagement in e-learning systems, and
what impact does this have on their cognitive skills?
 What are the key factors that influence student engagement in e-learning systems, and how can a deep learning-
based model be designed to effectively detect and enhance student engagement to improve their cognitive skills?
 What are the ethical implications of using deep learning-based models to detect and enhance student engagement in
e-learning systems, and how can these models be designed and implemented to mitigate potential ethical concerns and
ensure the protection of students' privacy and rights?
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13. Literature Review
Literature Review Methodology & Implementation Plan
Introduction The research focus on student engagement in e-learning and cognitive skill enhancement.
- Highlight the importance of these factors in online education.
Student Engagement in E-Learning
Three types of student engagement: behavioral, emotional, and cognitive (Fredricks,
Blumenfeld, & Paris, 2004).
High-impact educational practices for student engagement in e-learning (Kuh, 2009).
Personalized e-learning environments for increased engagement (Zhang et al., 2019).
Cognitive Skill Enhancement
- Introduce Bloom's Taxonomy of Cognitive Domain for cognitive skill development (Anderson
& Krathwohl, 2001).
- Emphasize the role of formative assessment in enhancing cognitive skills (Hattie, 2012).
- Discuss principles of multimedia learning for cognitive skill acquisition (Mayer, 2014).
Integration of Student Engagement and Cognitive Skill Enhancement
- Explain the relationship between engagement and cognitive development (Artino & Jones II,
2012).
- Explore learner-centered approaches in e-learning (Lim & Morris, 2009).
- Discuss the use of social constructivist principles for cognitive development (Dennen, Darabi,
& Smith, 2007).
Methodology for E-Learning Research
- Introduce the Community of Inquiry (CoI) framework for assessing e-learning quality
(Garrison & Anderson, 2003).
Data Set and Model Development - Prepare the "fer2013" dataset within Anaconda and Jupyter Notebook.
- Develop a Convolutional Neural Network (CNN) for emotion recognition using TensorFlow
and Keras.
Model Implementation - Train and evaluate the model using the "fer2013" dataset to recognize emotions in images.
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14. • There is a growing body of research on the relationship between student engagement and cognitive skill enhancement in e-learning.
Some key studies and findings include:
• A study by Fredricks et al. (2012) found that student engagement in e-learning was positively correlated with cognitive skills such as
critical thinking and problem-solving.
• A study by Spanjers (2007) found that students who were more engaged in e-learning courses performed better on cognitive skill
assessments.
• A study by Wang et al. (2020) found that a deep learning-based student engagement detection and enhancement model in an e-learning
system was effective in improving student engagement and learning outcomes, including cognitive skills.
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15. • The research suggests that student engagement is a key factor in cognitive skill enhancement
in e-learning. Effective e-learning systems should therefore focus on strategies to promote
student engagement.
• Strategies to Promote Student Engagement in E-Learning
• There are a number of strategies that can be used to promote student engagement in e-
learning. Some of these strategies include:
• Make the learning content relevant and engaging. Students are more likely to be engaged if
they are learning about something that they are interested in and that is relevant to their lives.
• Use a variety of learning activities. Students have different learning styles, so it is important
to use a variety of learning activities in e-learning courses. This could include lectures, readings,
discussions, quizzes, assignments, and projects.
• Provide opportunities for interaction and collaboration. Students learn better when they are
able to interact and collaborate with their peers and instructors. E-learning systems should
provide opportunities for students to interact with each other and with their instructors through
discussion forums, chat rooms, and other online tools.
• Provide timely feedback. Students need feedback on their work in order to learn and
improve. E-learning systems should provide students with timely feedback on their assignments
and participation in class activities.
• Create a supportive learning environment. Students are more likely to be engaged in e-
learning if they feel supported by their instructors and peers. E-learning systems should create a
supportive learning environment where students feel comfortable asking questions and seeking
help.
• By implementing these strategies, e-learning systems can promote student engagement and
cognitive skill enhancement.
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16. Research Methodology
 The research methodology used in this study is a quantitative approach. Quantitative research is a type of
research that uses quantitative data to answer research questions. Quantitative data is data that is numerical and
can be measured.
 The data source used in this study is a log dataset of student interactions with an e-learning system. The log
dataset contains information about student activities such as logging in, reading course materials, participating in
discussions, and completing assignments.
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17. Model Evaluation
The model is evaluated using a holdout dataset. The holdout dataset is a subset of the log dataset that was not
used to train the model. The model is evaluated on its ability to predict student engagement on the holdout
dataset.
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18. The deep learning model used in this study is a convolutional neural network (CNN). CNNs are a
type of neural network that is well-suited for image classification and other tasks that involve
spatial data.
The CNN model used in this study takes as input a sequence of student interactions with the e-
learning system. The model then learns to extract features from the sequence of interactions and
uses these features to predict student engagement.
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19. Why Deep Learning as the
Foundation for the Model
• Deep learning is a type of machine learning that uses artificial neural networks to learn from data. Neural networks
are inspired by the human brain and can be trained to detect patterns in data and make predictions.
• Deep learning is chosen as the foundation for the student engagement detection and enhancement model because it
has several advantages over other machine learning methods. Deep learning models can learn complex patterns in data
and make predictions with high accuracy. Additionally, deep learning models can be trained on large datasets, which is
important for this study as the log dataset contains a large amount of data.
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20. Deep Learning Model Architecture
To collect the data for the student engagement detection and enhancement model, we used a log dataset of
student interactions with an e-learning system. The log dataset contains information about student activities such
as logging in, reading course materials, participating in discussions, and completing assignments.
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22. Finding Facial Expressions
 Vision Based Techniques Model Classification
Problem
 Camera Input 1. 1.Angry
2. Happy
3. Sad
4.Surprised
5.Excited
• 6.Tired
Bio Signals/Physiological Signals
• PPG
• ECG Regression Problem
•
• EEG
Human Emotion Recognition
input
Deep
Learning
Deep
Learning
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24. Finding Facial Expressions
Vision Based Techniques
Model
Classification
Problem
Camera Input
1.Angry
•2. Happy
•3. Sad
4.Surprised
•5.Excited
6.Tired
Deep
Learning
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25. TYPES OF Facial Expressions
7 classes
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26. Dataset : First Step
Goodfellow, I.J. et al. (2013). Challenges in Representation Learning: A Report on Three Machine
Learning Contests. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing.
ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg.
https://doi.org/10.1007/978-3-642-42051-1_16
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27. Fer2013 Kaggle
Face Expression Recognition
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28. Methodology
(Transfer learning for face Emotion Recognition)
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