Machine Learning Based Recommendation System for Web-Search Learning
- Setting the learning objectives and goals.
- Locating and searching for the information through search engines and accessing the required information in the desired forms such as text, image, and video.
- Resources or information evaluation from the online resources accessed.
- Information processing and knowledge integration with other online resources.
- Synthesis and knowledge representation after the learning phase.
- To analyze the usage of these formats, some of the learner’s characteristics, such as the page or link navigations, learner eye movements, and language markup of traversed resources, are recorded during the simulation.
- To record the search time for the specific format. The proposed model automatically analyzes the learners’ interests while searching online and analyzes the origin of the acquired and the information learned online. This research performs text content mapping and video content.
- To analyze the efficiency of the eye tracker and to measure the characteristics of the learners—pupil dilation, visual blinking, eye movements, gazing point, visual attention of engaging and ignoring.
2. Literature Review
3. Materials and Methods
3.1. Novelty of the Proposed Model
- Evaluate and analyze the learner’s knowledge acquisition through the core operations, obtaining better measures using cluster-based recommendations.
- Store the complete track of online resources that are visited.
- Define the mapping between the information that has been newly learned to resources processing, using sensors.
- Store the sequence of words that the learners had learned online.
- Apply the video transcripts to keep track of the words visited through online videos.
- Analyze the overlapping between the traversed words and the recalled data.
3.2. Architecture of the Proposed Model
|Algorithm 1: Proposed Model—Resources Processing|
|1: Data or information logging|
1.1 Evaluate prior knowledge.
1.2 Perform online search learning.
1.3 Map online resources into log files and transcript information.
1.4 Evaluate the post-knowledge.
2: Processing the data or information.
2.1 Extract the words using the concept assessment.
2.2 Use the software to recognize the text, image, and video resources.
2.3 Extract the words using the post-knowledge assessment.
3: Define the mapping operation.
3.1 Remove the unnecessary words after preprocessing.
3.2 Perform the word match between video and webpages information.
3.3 Construct the word origin table with the resources.
3.3. Datasets and Learners’ Information
3.4. Learner’s Task and Information Processing
3.5. Mapping Operation
3.6. New Recommendation Model
|Algorithm 2: New clustered intelligent CF|
|Inputs: learner dataset, learner x, S, |
1: Input the learner’s rating information from the learner’s dataset and items.
2: Split the knowledge requirements of the learners from the datasets.
3: Evaluate linear cluster operation to find out the knowledge requirements for every partition and bring better requirements from the table.
4: Apply every learner y available in the learner dataset with y ≠ x in every cluster partition:
5: Evaluate using items separation for the learners x and y.
6: Calculate r using
7: Sort the learners reversely based on Equation (1).
8: Separate elements from List and update S.
9: Update the learner’s similar requirements in every partition.
10: Calculate such that j using (2)
11: Sort from Equation (2) in every partition, find the overall prediction rating for all items not rated.
12: Update the list from all partitions and select a better choice from the database evaluation.
13: Separate entries and update L.
4. Results and Discussion
4.1. Access to Online Resources
4.2. Recommender Model Analysis
4.3. Eye Tracker Efficiency Analysis
- The technology implemented in the eye tracker software measures the learners’ characteristics—pupil dilation, visual blinking, eye movements, gazing point, and visual attention of engaging and ignoring.
- The accuracy of the eye tracker is less than 0.5° in the controlled environments, with the actual gaze point offset frequently by at least 1°. The gazing point is sampled at different rates.
- The standard frame rate lies (60 Hz, 500 Hz) images per second. The frame rate of the web camera lies (5 Hz, 30 Hz).
- The learner’s performance in answering the questions is also analyzed. The repetition of gazing in choosing some options is a wavering characteristic of the learners when they are confused about choosing the option. The system predicted that 12% of the learners had the wavering characteristic, in which 7% of the learners failed to choose the correct option, and 5% chose the right option.
- The learner’s knowledge acquisition is evaluated as 88% ground truth responses and 12% underperformance.
- The learner’s engagement and findability characteristics in solving quizzes are analyzed as follows: 84% of the quick response learners, 16% of the learners are gazed or uninterested.
- The average response times of the simulation are observed as follows: choosing correct options (selectivity) 2.5 min, and choosing incorrect options lies 3 to 5 min. Sensitivity: 87% of the learners sensed the suitable options, 8% showed inattentional blindness, and 5% of the learners with had wavering gaze characteristics.
- The proposed model’s overall expected eye tracking or eye movement percentage in web-search learning is 88%.
5. Conclusions and Future Work
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Social Science||μ: 1.78, σ: 1.89|
|English||μ: 1.12, σ: 1.95|
|Mathematics||μ: 1.65, σ: 1.76|
|Management||μ: 1.34, σ: 1.65|
|Computer Science||μ: 1.92, σ: 1.98|
|Concept Groups||Quiz Q1||Quiz Q2|
|Concept Groups||Text||Videos||Overlapping Text or Video||Overall|
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M. R. M., V.; Rodriguez, C.; Navarro Depaz, C.; Concha, U.R.; Pandey, B.; S. Kharat, R.; Marappan, R. Machine Learning Based Recommendation System for Web-Search Learning. Telecom 2023, 4, 118-134. https://doi.org/10.3390/telecom4010008
M. R. M. V, Rodriguez C, Navarro Depaz C, Concha UR, Pandey B, S. Kharat R, Marappan R. Machine Learning Based Recommendation System for Web-Search Learning. Telecom. 2023; 4(1):118-134. https://doi.org/10.3390/telecom4010008Chicago/Turabian Style
M. R. M., Veeramanickam, Ciro Rodriguez, Carlos Navarro Depaz, Ulises Roman Concha, Bishwajeet Pandey, Reena S. Kharat, and Raja Marappan. 2023. "Machine Learning Based Recommendation System for Web-Search Learning" Telecom 4, no. 1: 118-134. https://doi.org/10.3390/telecom4010008