Socio- Technical System for Effective Classroom Learning / (Record no. 607360)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02457nam a22001697a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUST |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kainat. |
| 245 ## - TITLE STATEMENT | |
| Title | Socio- Technical System for Effective Classroom Learning / |
| Statement of responsibility, etc. | Kainat |
| 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 | 61p. ; |
| Other physical details | Soft Copy |
| Dimensions | 30cm. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Analyzing attention enables educators to assess student engagement and enhance<br/>learning experiences. It provides valuable insights for optimizing teaching and<br/>managing classroom behavior. Several techniques have been proposed to analyze<br/>attention and provide feedback to the instructor for effective learning. These include<br/>intrusive and non-intrusive techniques which utilize EEG headsets, eye trackers,<br/>Kinect sensors, cameras, non-verbal cues etc. Intrusive techniques provide accurate<br/>results only for controlled environments prioritizing precise measurements. Moreover,<br/>they cause discomfort to the subjects involved. Whereas non-intrusive techniques<br/>using non-verbal features do not cause any discomfort to the user and can be used in<br/>any environment. However, none of the studies so far have addressed all non-verbal<br/>features simultaneously. This paper presents a multimodal architecture which<br/>integrates all non-verbal features including headpose orientation, body posture<br/>estimation, emotion detection and Eye Aspect Ratio (EAR) calculation to analyze<br/>attention. A deep learning model has been trained on the Facial Expression<br/>Recognition Plus (FERPlus) dataset with 94% accuracy. We used Euler angles to<br/>determine the head pose which includes up, down, left, right and forward directions.<br/>Further EAR is calculated for both eyes using eye key points and Euclidean distance<br/>which shows the opening and closing state of the eyes. Finally estimated the body<br/>pose of the student by training an SVM model & body key points which include<br/>shoulders, elbows, and wrists. The combined result of all these features is displayed in<br/>the form of a graph which reflects the level of attentiveness of the students to the<br/>teacher in real-time. This system can assist the teacher in addressing concerns such as<br/>poor academic performance, disengagement from studies, and high dropout rates<br/>among students. |
| 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. Sara Ali |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/33974">http://10.250.8.41:8080/xmlui/handle/123456789/33974</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 | 12/13/2023 | 629.8 | SMME-TH-859 | Thesis |