Maximizing Reusability of Learning Objects Through Machine Learning Techniques

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Oct 13 2023

In a paper published in the journal Scientific Reports, researchers highlighted the transformative impact of machine learning (ML) techniques on e-learning systems, which boosted the reusability of learning objects by employing web exploration algorithms to extract metadata, using feature selection to reduce dataset dimensionality. ML algorithms categorized objects based on similarity, outperforming traditional methods and enhancing learning object reusability, as demonstrated through real-world dataset experiments.

Background

The rapid expansion of distance-learning systems and the availability of massive open online courses have greatly influenced the sharing and reusability of learning objects as essential educational resources. However, the efficient utilization of these objects in e-learning environments remains challenging due to the need for systematic categorization. This study employs cutting-edge ML algorithms for classification, using metadata and web mining techniques to enhance learning object organization.

Previous research has centered on learning objects and digital pedagogical tools with diverse forms and applications. These objects are typically self-contained units of learning, described by metadata for classification, and are used to create complex educational entities. The field has seen the application of ML techniques for learning object classification, employing various classifiers, including decision trees, neural networks, and Long Short-Term Memory (LSTM) with Random Forest. Many researchers have actively explored Multi-Label Classification (MLC) paradigms to improve accessibility and provide recommendations for learning objects.

Proposed Method

This study aims to tackle the challenges surrounding using learning objects in educational resource development, offering fresh insights into areas that have yet to receive much attention in previous research. One of its principal innovations lies in exploring the intricacies of learning object reuse, encompassing the level of detail and granularity involved. The research delves into the nuances of what gets reused and how it is replicated, interconnected, blended, and published, thereby shedding light on the complexities of content repurposing. Another novel dimension addressed is the identification of the various stakeholders participating in learning object reuse, providing a deeper understanding of their roles in the content recycling process.

Furthermore, the study delves into the geographical and situational aspects of learning object reuse, examining the exchange of materials among individuals and organizations and the distinctions between different learning contexts. Lastly, the research introduces the element of timing into the investigation, analyzing when materials become available for reuse to enhance the comprehension of the temporal aspects of content reuse. This exploration is conducted through ML techniques, aiming to unveil patterns and relationships within the data for a more structured understanding of learning object reuse, paving the way for more efficient and targeted strategies to enhance learning experiences.

Web Data Mining Overview: Web Data Mining, a field initially defined in 1996, focuses on extracting valuable information from the rapidly growing volumes of digital data. This process involves three key steps: data pre-processing, pattern discovery, and pattern analysis. Pre-processing tasks include actions such as normalizing learning object metadata. These tasks are crucial in reducing data dimensions by eliminating extraneous information.

The pattern discovery phase uses ML algorithms to label the data. Finally, in the pattern analysis step, appropriate patterns are presented based on the degree of similarity. This process is instrumental in organizing and making sense of data, enabling extracting meaningful insights and knowledge from vast datasets.

Fuzzy C-Means (FCM) and MLC: FCM is an unsupervised, non-hierarchical clustering algorithm that groups data into fuzzy clusters based on common characteristics. FCM aims to partition data into clusters based on a predefined criterion while iteratively updating cluster centers until it reaches convergence.

On the other hand, MLC is an approach that uses supervised learning to assign multiple labels to objects based on their features, making it particularly relevant for learning object classification. While MLC has found extensive use in various fields, its application in e-learning remains relatively unexplored, underscoring its potential significance in learning object classification within educational contexts.

Experimental Results

Dataset Overview: This research study gathered a comprehensive dataset from various sources, including Moodle, Blackboard, and Schoology, encompassing various learning object models across different categories. Prominent companies that specialize in educational solutions and platforms provide these datasets. This diverse dataset enriches the study's ability to comprehensively explore learning object classification using ML techniques, enhancing the overall relevance and impact of the research findings.

Experiment and Performance Assessment: A set of performance indicators, including classification accuracy, precision, recall, and F1-measure, was employed to assess the approach's effectiveness. These indicators provided a comprehensive assessment of the model's performance compared to existing systems. The study considered different ML algorithms such as Support Vector Machine (SVM), Random Forest, and Naive Bayes across three distinct datasets (Moodle, Blackboard, and Schoology). The results showcased SVM's remarkable accuracy in various datasets, with SVM achieving high precision, recall, and F1 scores, making it a dependable choice for learning object classification. Additionally, integrating FCM with SVM or Random Forest models led to notable performance improvements, emphasizing the effectiveness of incorporating fuzzy clustering techniques to enhance classification accuracy and identify learning objects.

Conclusion

In summary, this research underscores the pivotal role of advanced ML techniques, including MLC and FCM, in revolutionizing the classification and recommendation of learning objects in e-learning systems. ML enables the efficient organization of learning materials and enhances their discoverability for learners, making them more relevant and accessible. Integrating FCM refines the classification process, reducing data volume and ensuring meaningful and contextually appropriate recommendations. Evaluation using data from renowned platforms affirms the superior performance of the proposed ML-driven approach compared to traditional algorithms, ultimately facilitating a more personalized and efficient learning experience.