Focus and Engagement Level Detection Using Computer Vision and Machine Learning in a Classroom Environment / (Record no. 607417)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02194nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Poonja, Hasnain Ali |
| 245 ## - TITLE STATEMENT | |
| Title | Focus and Engagement Level Detection Using Computer Vision and Machine Learning in a Classroom Environment / |
| Statement of responsibility, etc. | Hasnain Ali Poonja |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 64p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Due to Covid 19, the global education system has changed toward online learning, which<br/>has a high dropout rate. Therefore, it is vital that students maintain their level of interest. Therefore,<br/>detection of engagement level alone is insufficient for analyzing and improving learning and<br/>teaching techniques. To promote student engagement in STEM and online learning environments,<br/>technologies such as AR/VR and Haptics should be implemented. Utilizing facial emotion, body<br/>pose, and head rotation, a web-based computer vision system is developed and implemented to<br/>identify student involvement levels using webcams during tasks such as online classrooms, haptic<br/>interaction, and augmented reality. In addition, an AR and Haptics-based World Map is being<br/>designed and developed. To evaluate and compare three types of learning scenarios, namely (1)<br/>Traditional, (2) Augmented Reality-based, and (3) Haptics-based, two methods are employed: (1)<br/>Trained Computer Vision models are tested for 3 scenarios, and (2) A user study is conducted<br/>using the Positive and Negative Affect Schedule (PANAS) Questionnaire and NASA-Task Load<br/>Index, from which conclusions are drawn.<br/>The results of a comparison of Traditional, Augmented reality, and Haptics-based learning<br/>indicate that Haptics and Augmented Reality-based learning are the most immersive and increase<br/>levels of engagement during online learning and STEM training, whereas Traditional learning<br/>methods are the least effective during online classes. User studies and computer vision models are<br/>utilized to validate the results. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Robotics and Intelligent Machine Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Muhammad Jawad Khan |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/33689">http://10.250.8.41:8080/xmlui/handle/123456789/33689</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 01/17/2024 | 629.8 | SMME-TH-850 | Thesis |