The document proposes developing a deep learning-based student engagement detection model for e-learning systems. It aims to more accurately assess student engagement levels in real-time compared to traditional methods. The objectives are to evaluate the model's accuracy in detecting engagement, assess its impact on cognitive skills like retention, and identify areas for improvement. A convolutional neural network model would take student interaction sequences as input to predict engagement. The research hypothesizes that the model would significantly improve cognitive skills versus traditional methods by providing personalized feedback to re-engage disengaged students.
Analysis of Learning Behavior Characteristics and Prediction of Learning Effe...Shakas Technologies
Analysis of Learning Behavior Characteristics and Prediction of Learning Effect for Improving College Students’ Information Literacy Based on Machine Learning.
Dr. William Allan Kritsonis, Editor-in-Chief, NATIONAL FORUM JOURNALS (Founded 1982). Dr. Kritsonis has served as an elementary school teacher, elementary and middle school principal, superintendent of schools, director of student teaching and field experiences, professor, author, consultant, and journal editor. Dr. Kritsonis has considerable experience in chairing PhD dissertations and master thesis and has supervised practicums for teacher candidates, curriculum supervisors, central office personnel, principals, and superintendents. He also has experience in teaching in doctoral and masters programs in elementary and secondary education as well as educational leadership and supervision. He has earned the rank as professor at three universities in two states, including successful post-tenure reviews.
1) The document discusses big data and learning analytics in education, including how it has been featured in the NMC Horizon Report from 2010-2013. It describes how big data can be used for educational research purposes such as modeling student knowledge, behavior, experiences, profiling student groups, and analyzing learning components and instructional principles.
2) Examples of learning analytics in practice are provided, including Purdue University's Signals project, Saddleback Community College's personalized learning system, and analytics tools used at other universities.
3) Potential applications of learning analytics discussed include using data to provide insights into student reading habits, facilitating anonymous peer feedback and grading in writing courses, and capturing data to engage students in interactive teaching situations.
Bridging the gap of the educational system across different countries through...PhD Assistance
The gap in the educational system has been a major drawback globally. The idea and concept of E-Learning have been evolved as a result of many kinds of Research. E-learning has assisted in closing this gap. The main goal of the study is to offer quality education through e-learning by assessing the effectiveness of e-learning mode. The focus has been to assess the e-learning potential to provide a quality education through electronic means and also to evaluate the scope of e-learning. E-learning provides a better standard of living for students across the world. This paper deals with improving the student’s quality of education and their standard of living
Visite : https://www.phdassistance.com/blog/
Contact Us:
UK NO: +44-1143520021
India No: +91-8754446690
Email: info@phdassistance.com
This study investigated female students' attitudes toward a web-enhanced educational technology course at the United Arab Emirates University. 66 female students enrolled in three sections of the course completed a survey about their experiences. The researchers found that incorporating web-based components into the traditional face-to-face course was viewed positively by students and enriched the learning environment. Prior computer experience, learning preferences, and experience with web-enhanced courses influenced students' attitudes. While the methodology provided useful findings, interviews may have provided additional insights into factors shaping students' perspectives. The study demonstrated educational benefits to a blended approach but noted support and resources would need to continue improving for long-term success.
This document summarizes a journal article that examines tutors' views on utilizing e-learning systems in architectural education. The study surveyed tutors from the architecture faculty at a university in Saudi Arabia. It found that many tutors had limited experience using online tools and a slightly better experience with communication tools. While tutors were against online design courses, a mix of traditional and online teaching could provide more student support. The study concluded that innovative tools and a strategy integrating professional training and education are needed. Further research should assess blended courses and develop new systems to overcome shortcomings and meet architectural education needs.
Big Data and Advanced Analytics For Improving Teaching Practices In 2023 | Fu...Future Education Magazine
Here are 7 ways of big data and advanced analytics to improve teaching practices: 1. Data Sources in Education 2. The Role of Big Data in Education 3. Advanced Analytics in Education 4. Assessing Teaching Practices with Data 5. Enhancing Teaching Practices with Data
Unlocking Educational Potential: A Comprehensive Guide to Learning AnalyticsFuture Education Magazine
Learning Analytics refers to the measurement, collection, analysis, and reporting of data about learners and their contexts for understanding and optimizing learning and the environments in which it occurs.
Analysis of Learning Behavior Characteristics and Prediction of Learning Effe...Shakas Technologies
Analysis of Learning Behavior Characteristics and Prediction of Learning Effect for Improving College Students’ Information Literacy Based on Machine Learning.
Dr. William Allan Kritsonis, Editor-in-Chief, NATIONAL FORUM JOURNALS (Founded 1982). Dr. Kritsonis has served as an elementary school teacher, elementary and middle school principal, superintendent of schools, director of student teaching and field experiences, professor, author, consultant, and journal editor. Dr. Kritsonis has considerable experience in chairing PhD dissertations and master thesis and has supervised practicums for teacher candidates, curriculum supervisors, central office personnel, principals, and superintendents. He also has experience in teaching in doctoral and masters programs in elementary and secondary education as well as educational leadership and supervision. He has earned the rank as professor at three universities in two states, including successful post-tenure reviews.
1) The document discusses big data and learning analytics in education, including how it has been featured in the NMC Horizon Report from 2010-2013. It describes how big data can be used for educational research purposes such as modeling student knowledge, behavior, experiences, profiling student groups, and analyzing learning components and instructional principles.
2) Examples of learning analytics in practice are provided, including Purdue University's Signals project, Saddleback Community College's personalized learning system, and analytics tools used at other universities.
3) Potential applications of learning analytics discussed include using data to provide insights into student reading habits, facilitating anonymous peer feedback and grading in writing courses, and capturing data to engage students in interactive teaching situations.
Bridging the gap of the educational system across different countries through...PhD Assistance
The gap in the educational system has been a major drawback globally. The idea and concept of E-Learning have been evolved as a result of many kinds of Research. E-learning has assisted in closing this gap. The main goal of the study is to offer quality education through e-learning by assessing the effectiveness of e-learning mode. The focus has been to assess the e-learning potential to provide a quality education through electronic means and also to evaluate the scope of e-learning. E-learning provides a better standard of living for students across the world. This paper deals with improving the student’s quality of education and their standard of living
Visite : https://www.phdassistance.com/blog/
Contact Us:
UK NO: +44-1143520021
India No: +91-8754446690
Email: info@phdassistance.com
This study investigated female students' attitudes toward a web-enhanced educational technology course at the United Arab Emirates University. 66 female students enrolled in three sections of the course completed a survey about their experiences. The researchers found that incorporating web-based components into the traditional face-to-face course was viewed positively by students and enriched the learning environment. Prior computer experience, learning preferences, and experience with web-enhanced courses influenced students' attitudes. While the methodology provided useful findings, interviews may have provided additional insights into factors shaping students' perspectives. The study demonstrated educational benefits to a blended approach but noted support and resources would need to continue improving for long-term success.
This document summarizes a journal article that examines tutors' views on utilizing e-learning systems in architectural education. The study surveyed tutors from the architecture faculty at a university in Saudi Arabia. It found that many tutors had limited experience using online tools and a slightly better experience with communication tools. While tutors were against online design courses, a mix of traditional and online teaching could provide more student support. The study concluded that innovative tools and a strategy integrating professional training and education are needed. Further research should assess blended courses and develop new systems to overcome shortcomings and meet architectural education needs.
Big Data and Advanced Analytics For Improving Teaching Practices In 2023 | Fu...Future Education Magazine
Here are 7 ways of big data and advanced analytics to improve teaching practices: 1. Data Sources in Education 2. The Role of Big Data in Education 3. Advanced Analytics in Education 4. Assessing Teaching Practices with Data 5. Enhancing Teaching Practices with Data
Unlocking Educational Potential: A Comprehensive Guide to Learning AnalyticsFuture Education Magazine
Learning Analytics refers to the measurement, collection, analysis, and reporting of data about learners and their contexts for understanding and optimizing learning and the environments in which it occurs.
The Role of Data Science in the Future of E-Learning Analytics.pdfkherbalspiceltd
Dive into the future of work with comprehensive insights on professional development, education, data science, digital marketing, finance, artificial intelligence, and entrepreneurship. Transform your potential into expertise today – where learning meets innovation.
Administrator Work In Leveraging Technologies For Students With Disabilities ...Nathan Mathis
This study examined how online administrators supported teachers in providing technology-based accommodations for students with disabilities. The researchers interviewed four special education teachers and analyzed accommodation plans from student IEP documents over four months. They found that (1) providing technology accommodations required intensive collaboration, (2) teachers struggled to implement all mandated accommodations while also using supportive technologies, and (3) technology accommodations were often limited to tools already available to all students. The implications are that transferring IEPs to online environments is complex, and online learning is not inherently accommodating without careful consideration at all levels.
STUDENT ENGAGEMENT MONITORING IN ONLINE LEARNING ENVIRONMENT USING FACE DETEC...IRJET Journal
This document discusses a proposed system to monitor student engagement in online learning environments using face detection. The system would use face recognition and head pose estimation to authenticate that students are present and attentive during online lectures. Student engagement is important for learning outcomes, but more difficult to monitor online compared to in-person. The proposed system would collect data on attention, emotions, and activities to provide insights on class and student engagement levels. This could help instructors evaluate their teaching methods and identify students who may need extra support. The document outlines the implementation of this system using tools like the DAiSEE dataset for emotion detection and analyzing head pose to estimate attentiveness. It also provides examples of what the instructor dashboard and student interfaces may look like.
This document discusses learning analytics, which involves measuring, retrieving, collecting, and analyzing student data from various learning environments. Learning analytics can help educators track student progress and behavior to improve instruction and support. However, there are also challenges around data storage, privacy, and ensuring analytics are aligned with educational goals. Opportunities exist to capture more detailed behavioral data through tools, but institutions must have the capacity to maintain analytics systems and apply insights pedagogically.
The article critiques a journal article that examines tutors' views on utilizing e-learning systems in architectural education. The critiqued article surveyed tutors from a university's architecture program to understand barriers to adopting e-learning tools and how to encourage use. It found that many tutors lacked experience with e-learning platforms and online resources, but had better skills with communication tools. The critiqued article concluded a mix of traditional and online teaching could provide more support to students, and a clear strategy is needed to integrate online courses into architectural education.
"The Influence of Online Studies and Information using Learning Analytics"Fahmi Ahmed
This research will help people with inadequate knowledge to get
a better understanding of online study or e-learning. Through this
study, the social impact of online users or learners can be
increased, and the users can have a clear idea of online study. In
this research, the graphs will be presented according to country,
gender, age, online resources, etc. showing the impact of online
study and information on online users. The learners will get an
understandable knowledge of the type of sources, what is their
purpose, and resources people can use in online study. From this,
the learners will get a guide or path that how easily they can learn
online for study in a more flexible way. The outcomes are
visualized using the R language and Tableau with pre-processed
data.
#ForOurFuture18 UL System Conference Presentation: Online Learning - Current ...Luke Dowden
Two veterans of online learning will share their thoughts on the current state and the future of online learning. Chief online
learning officers face ongoing challenges growing, sustaining, and innovating online programs. Now that online learning
has entered the mainstream, what is its future? What fads will fade? What trends will be sustained? The audience will be
engaged throughout the presentation with opportunities to discuss the impact online learning has on technological
infrastructure, faculty support, course design, quality assurance / quality control, organizational structures, funding and
grants, and research. By sharing their experiences and insights into the current challenges and future state of online
learning, the presenters will discuss strategic and operational approaches to navigate current and future realities of online
learning. Credit to Dr. Darlene Williams for content on Future Opportunities and Context.
The document discusses a study on students' perceptions and attitudes towards computer-assisted learning among Grade 11 students in Old Damulog National High School in the Philippines. It begins with an introduction that provides background information on computer-assisted learning and its benefits. It then states the objectives, significance and limitations of the study. The results and discussion section analyzes students' perceptions based on survey responses. It found that students strongly agreed that computers can increase their interest in learning and chances of future career opportunities, but were undecided on whether computers distract them or if they have enough skills to use computers. Overall, the study aimed to examine students' views of using technology to aid their education.
CEMCA EdTech Notes: Learning Analytics for Open and Distance EducationCEMCA
Learning analytics use large datasets from learning management systems to improve learning and teaching. They focus on providing "actionable intelligence" through metrics, reports, and recommendations. Effective use of learning analytics requires consideration of context, people, and learning design. While learning analytics have potential to enhance education, they also raise issues regarding teaching models, learner privacy, and ensuring analytics do not reinforce biases.
A Survey on Research work in Educational Data Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document summarizes research on educational data mining. It discusses topics such as student modeling, improving educational software, mining assessment data, and generic frameworks/methods. Student modeling research focuses on automatically improving student models and predicting student performance. Research on improving software examines identifying learning behaviors and adapting intelligent tutoring systems based on individual differences. Assessment data mining analyzes optimal/worst-case mastery learning and predicting dropout using social behavior data. Generic frameworks include knowledge tracing approaches and tools for visualizing interaction networks. The conclusion recommends continued collaboration across research, education, and industry to further the field.
Report on the Exploratory Research project on Online Learning in southwest MB, 9-12.
Presented to Southwest MB School Superintendents & Web-Based Contacts Meeting for MB Education. December, 2009.
The search for knowledge is an everyday thing amongst humans. This has resulted in the enrolment of people into different institutions of learning. The development of technology and the discovery of the internet resulted in their usage for learning. Several institutions have implemented this in their programmes over the years. This paper examined the concept of e-learning; how it works, it benefits to learners, educators and the society and some challenges it faces. Consequently, its workability for mathematics educators was deduced.
Computers are a familiar sight in classrooms in the 21st century, and technology has been used to streamline many educational tasks. CAL started in the 1950s and 1960s mainly in USA. Term often used interchangeably with Computer-Based Instruction (CBI), Web Based Instruction (WBI), Computer-Assisted Learning (CAL), Computer-Enriched Instruction (CEI), and Web Based Training (WBT). Logo project was the first CAL system that was based on a specific learning approach.
OLAP based Scaffolding to support Personalized Synchronous e-Learning IJMIT JOURNAL
The advent of asynchronous web based learning systems has helped the learner in a self paced,
personalized and flexible learning style. It can be even more useful with a supportive synchronous tutorial
(question-answer) session. The challenge is to provide sufficient information to the instructor about the
learner’s experience in that particular course at run time. Online analytical processing (OLAP) is a very
useful technique in producing such run time information in the form of reports. In this paper we have
designed an automated scaffolding technique to hold this vital information about the learner which we have
obtained by OLAP techniques on the log data of the LMS users. We have also proposed an overall
architecture of the scaffolding where this information can be easily accessed and used by the instructor in
the synchronous tutorial session to make the system more adaptive.
This document proposes using text analytics and the RapidMiner data analytics tool to analyze student data from an online learning environment to predict students' interests in various subject areas. It discusses limitations in current approaches and the need to more accurately understand student interests to refine educational offerings. The proposed approach would collect student data through the UTS online platform and use text analytics and RapidMiner to identify patterns in students' discussions that indicate their interests in different topics. This could help university authorities better tailor course content based on predicted student demand.
Designing a Scaffolding for Supporting Personalized Synchronous e-Learningcscpconf
The advent of asynchronous web based learning systems has helped the learner in a self paced,
personalized and flexible learning style. It can be even more useful with a supportive
synchronous tutorial (question-answer) session. The challenge is to provide sufficient
information to the instructor about the learner’s experience in that particular course. In this paper we have designed an automated scaffolding technique to hold these vital information’s about the learner which can be accessed and used by the instructor in the synchronous tutorial session to make the system more adaptive.
This research proposal examines different models of online education to determine the most effective for educating students. It analyzes the behaviorism, constructivism, and blended learning models. The blended learning model incorporates different delivery methods like online courses and knowledge management. It has three sub-models: skill-driven focuses on instructor-student interaction; behavior-driven blends traditional and technology-enabled events; and competency-driven has students learn from experts. The study aims to evaluate online education quality by surveying student feedback to conclude it provides convenient, accessible education regardless of status. The methodology involves an online survey of students enrolled in online programs.
The document discusses using analytics to improve student retention and other outcomes at the University of Kentucky. It describes:
1) Initial steps taken over the past year, including mobile surveys of students, analyzing enrollment, performance, and other data, and using high-speed analytics.
2) Potential future areas of work, such as personalized mobile interactions with students, advanced models of student behavior and performance, improved lecture content discovery and recommendations, and degree planning tools.
3) Various data models already in use, including those tracking enrollment, student demographics and performance, classroom and instructor productivity, and student surveys and interactions.
This document outlines a research proposal for a study on the impact of flipped classrooms and mobile learning. The study will take place at Sharjah Women's College in the UAE, which has about 2,000 female students between ages 17-25 from Sharjah and surrounding emirates. Through needs analysis including interviews and observations, the author identified flipped classroom and mobile learning using Moodle as an approach to potentially improve student English skills. The research will use a correlation study to examine relationships between variables like age, gender, language and students' use of reading articles or videos in Moodle discussions. Data will be collected through surveys and observations, following ethics guidelines to protect student privacy and confidentiality. The expected results could benefit education in
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
Mais conteúdo relacionado
Semelhante a 1st Seminar Presentation By Ali Aijaz Shar [Autosaved].pptx
The Role of Data Science in the Future of E-Learning Analytics.pdfkherbalspiceltd
Dive into the future of work with comprehensive insights on professional development, education, data science, digital marketing, finance, artificial intelligence, and entrepreneurship. Transform your potential into expertise today – where learning meets innovation.
Administrator Work In Leveraging Technologies For Students With Disabilities ...Nathan Mathis
This study examined how online administrators supported teachers in providing technology-based accommodations for students with disabilities. The researchers interviewed four special education teachers and analyzed accommodation plans from student IEP documents over four months. They found that (1) providing technology accommodations required intensive collaboration, (2) teachers struggled to implement all mandated accommodations while also using supportive technologies, and (3) technology accommodations were often limited to tools already available to all students. The implications are that transferring IEPs to online environments is complex, and online learning is not inherently accommodating without careful consideration at all levels.
STUDENT ENGAGEMENT MONITORING IN ONLINE LEARNING ENVIRONMENT USING FACE DETEC...IRJET Journal
This document discusses a proposed system to monitor student engagement in online learning environments using face detection. The system would use face recognition and head pose estimation to authenticate that students are present and attentive during online lectures. Student engagement is important for learning outcomes, but more difficult to monitor online compared to in-person. The proposed system would collect data on attention, emotions, and activities to provide insights on class and student engagement levels. This could help instructors evaluate their teaching methods and identify students who may need extra support. The document outlines the implementation of this system using tools like the DAiSEE dataset for emotion detection and analyzing head pose to estimate attentiveness. It also provides examples of what the instructor dashboard and student interfaces may look like.
This document discusses learning analytics, which involves measuring, retrieving, collecting, and analyzing student data from various learning environments. Learning analytics can help educators track student progress and behavior to improve instruction and support. However, there are also challenges around data storage, privacy, and ensuring analytics are aligned with educational goals. Opportunities exist to capture more detailed behavioral data through tools, but institutions must have the capacity to maintain analytics systems and apply insights pedagogically.
The article critiques a journal article that examines tutors' views on utilizing e-learning systems in architectural education. The critiqued article surveyed tutors from a university's architecture program to understand barriers to adopting e-learning tools and how to encourage use. It found that many tutors lacked experience with e-learning platforms and online resources, but had better skills with communication tools. The critiqued article concluded a mix of traditional and online teaching could provide more support to students, and a clear strategy is needed to integrate online courses into architectural education.
"The Influence of Online Studies and Information using Learning Analytics"Fahmi Ahmed
This research will help people with inadequate knowledge to get
a better understanding of online study or e-learning. Through this
study, the social impact of online users or learners can be
increased, and the users can have a clear idea of online study. In
this research, the graphs will be presented according to country,
gender, age, online resources, etc. showing the impact of online
study and information on online users. The learners will get an
understandable knowledge of the type of sources, what is their
purpose, and resources people can use in online study. From this,
the learners will get a guide or path that how easily they can learn
online for study in a more flexible way. The outcomes are
visualized using the R language and Tableau with pre-processed
data.
#ForOurFuture18 UL System Conference Presentation: Online Learning - Current ...Luke Dowden
Two veterans of online learning will share their thoughts on the current state and the future of online learning. Chief online
learning officers face ongoing challenges growing, sustaining, and innovating online programs. Now that online learning
has entered the mainstream, what is its future? What fads will fade? What trends will be sustained? The audience will be
engaged throughout the presentation with opportunities to discuss the impact online learning has on technological
infrastructure, faculty support, course design, quality assurance / quality control, organizational structures, funding and
grants, and research. By sharing their experiences and insights into the current challenges and future state of online
learning, the presenters will discuss strategic and operational approaches to navigate current and future realities of online
learning. Credit to Dr. Darlene Williams for content on Future Opportunities and Context.
The document discusses a study on students' perceptions and attitudes towards computer-assisted learning among Grade 11 students in Old Damulog National High School in the Philippines. It begins with an introduction that provides background information on computer-assisted learning and its benefits. It then states the objectives, significance and limitations of the study. The results and discussion section analyzes students' perceptions based on survey responses. It found that students strongly agreed that computers can increase their interest in learning and chances of future career opportunities, but were undecided on whether computers distract them or if they have enough skills to use computers. Overall, the study aimed to examine students' views of using technology to aid their education.
CEMCA EdTech Notes: Learning Analytics for Open and Distance EducationCEMCA
Learning analytics use large datasets from learning management systems to improve learning and teaching. They focus on providing "actionable intelligence" through metrics, reports, and recommendations. Effective use of learning analytics requires consideration of context, people, and learning design. While learning analytics have potential to enhance education, they also raise issues regarding teaching models, learner privacy, and ensuring analytics do not reinforce biases.
A Survey on Research work in Educational Data Miningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document summarizes research on educational data mining. It discusses topics such as student modeling, improving educational software, mining assessment data, and generic frameworks/methods. Student modeling research focuses on automatically improving student models and predicting student performance. Research on improving software examines identifying learning behaviors and adapting intelligent tutoring systems based on individual differences. Assessment data mining analyzes optimal/worst-case mastery learning and predicting dropout using social behavior data. Generic frameworks include knowledge tracing approaches and tools for visualizing interaction networks. The conclusion recommends continued collaboration across research, education, and industry to further the field.
Report on the Exploratory Research project on Online Learning in southwest MB, 9-12.
Presented to Southwest MB School Superintendents & Web-Based Contacts Meeting for MB Education. December, 2009.
The search for knowledge is an everyday thing amongst humans. This has resulted in the enrolment of people into different institutions of learning. The development of technology and the discovery of the internet resulted in their usage for learning. Several institutions have implemented this in their programmes over the years. This paper examined the concept of e-learning; how it works, it benefits to learners, educators and the society and some challenges it faces. Consequently, its workability for mathematics educators was deduced.
Computers are a familiar sight in classrooms in the 21st century, and technology has been used to streamline many educational tasks. CAL started in the 1950s and 1960s mainly in USA. Term often used interchangeably with Computer-Based Instruction (CBI), Web Based Instruction (WBI), Computer-Assisted Learning (CAL), Computer-Enriched Instruction (CEI), and Web Based Training (WBT). Logo project was the first CAL system that was based on a specific learning approach.
OLAP based Scaffolding to support Personalized Synchronous e-Learning IJMIT JOURNAL
The advent of asynchronous web based learning systems has helped the learner in a self paced,
personalized and flexible learning style. It can be even more useful with a supportive synchronous tutorial
(question-answer) session. The challenge is to provide sufficient information to the instructor about the
learner’s experience in that particular course at run time. Online analytical processing (OLAP) is a very
useful technique in producing such run time information in the form of reports. In this paper we have
designed an automated scaffolding technique to hold this vital information about the learner which we have
obtained by OLAP techniques on the log data of the LMS users. We have also proposed an overall
architecture of the scaffolding where this information can be easily accessed and used by the instructor in
the synchronous tutorial session to make the system more adaptive.
This document proposes using text analytics and the RapidMiner data analytics tool to analyze student data from an online learning environment to predict students' interests in various subject areas. It discusses limitations in current approaches and the need to more accurately understand student interests to refine educational offerings. The proposed approach would collect student data through the UTS online platform and use text analytics and RapidMiner to identify patterns in students' discussions that indicate their interests in different topics. This could help university authorities better tailor course content based on predicted student demand.
Designing a Scaffolding for Supporting Personalized Synchronous e-Learningcscpconf
The advent of asynchronous web based learning systems has helped the learner in a self paced,
personalized and flexible learning style. It can be even more useful with a supportive
synchronous tutorial (question-answer) session. The challenge is to provide sufficient
information to the instructor about the learner’s experience in that particular course. In this paper we have designed an automated scaffolding technique to hold these vital information’s about the learner which can be accessed and used by the instructor in the synchronous tutorial session to make the system more adaptive.
This research proposal examines different models of online education to determine the most effective for educating students. It analyzes the behaviorism, constructivism, and blended learning models. The blended learning model incorporates different delivery methods like online courses and knowledge management. It has three sub-models: skill-driven focuses on instructor-student interaction; behavior-driven blends traditional and technology-enabled events; and competency-driven has students learn from experts. The study aims to evaluate online education quality by surveying student feedback to conclude it provides convenient, accessible education regardless of status. The methodology involves an online survey of students enrolled in online programs.
The document discusses using analytics to improve student retention and other outcomes at the University of Kentucky. It describes:
1) Initial steps taken over the past year, including mobile surveys of students, analyzing enrollment, performance, and other data, and using high-speed analytics.
2) Potential future areas of work, such as personalized mobile interactions with students, advanced models of student behavior and performance, improved lecture content discovery and recommendations, and degree planning tools.
3) Various data models already in use, including those tracking enrollment, student demographics and performance, classroom and instructor productivity, and student surveys and interactions.
This document outlines a research proposal for a study on the impact of flipped classrooms and mobile learning. The study will take place at Sharjah Women's College in the UAE, which has about 2,000 female students between ages 17-25 from Sharjah and surrounding emirates. Through needs analysis including interviews and observations, the author identified flipped classroom and mobile learning using Moodle as an approach to potentially improve student English skills. The research will use a correlation study to examine relationships between variables like age, gender, language and students' use of reading articles or videos in Moodle discussions. Data will be collected through surveys and observations, following ethics guidelines to protect student privacy and confidentiality. The expected results could benefit education in
Semelhante a 1st Seminar Presentation By Ali Aijaz Shar [Autosaved].pptx (20)
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
1st Seminar Presentation By Ali Aijaz Shar [Autosaved].pptx
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
1
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
4
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.
5
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.
7
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
8
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.
9
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”.
10
11. 11
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?
12
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.
13
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.
14
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.
15
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.
16
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.
17
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.
18
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.
19
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.
20
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
22
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
24
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
26
What is Holdout Data? Holdout data refers to a portion of historical, labeled data that is held out of the data sets used for training and validating supervised machine learning models. It can also be called test data.