With the advent of the era of big data and Web 3.0 on the horizon, different types of online deliverable resources in the pedagogical field have also become raft. Massive Open Online Courses (MOOCs) are the most important of such learning resources that provide many courses at different levels for the learners on the go. The data generated by these MOOCs, however, is often unorganized and difficult to track or is not used to the extent that allows identification of learner types to facilitate better learning. The proposed approach in this paper aims to detect the learning style of a learner, interacting with the MOOC portal, dynamically and automatically through a novel, indigenous and in-built browser extension. This extension is used to capture the usage parameters of the learner and analyze learning behavior in real-time. The usage parameters are captured and stored as a learner ontology to ease sharing and operating across different platforms. The learning style so deduced is based on the Felder Silverman Learning Style Model (FSLSM), where learner’s behavior under multiple criteria, vis-`a-vis perception, input, understanding, and processing are measured. Based on the generated ontological semantics of learner’s behavior, multiple models can be made to facilitate precise and efficient learning. The result shows that this state-of-the-art approach identifies and detects the learning styles of the learners automatically and dynamically, i.e., changing over time
The idea of data to be semantically linked and the subsequent usage of this linked data with modern computer applications has been one of the most important aspects of Web 3.0. However, the actualization of this aspect has been challenging due to the difficulties associated with building knowledge bases and using formal languages to query them. In this regard, SPARQL, a recursive acronym for standard query language and protocol for Linked Open Data and Resource Description Framework databases, is a most popular formal querying language. Nonetheless, writing SPARQL queries is known to be difficult, even for experts. Natural language query formalization, which involves semantically parsing natural language queries to their formal language equivalents, has been an essential step in overcoming this steep learning curve. Recent work in the field has seen the usage of artificial intelligence (AI) techniques for language modelling with adequate accuracy. This paper discusses a design for creating a closed domain ontology, which is then used by an AI-powered chat-bot that incorporates natural language query formalization for querying linked data using Rasa for entity extraction after intent recognition. A precision–recall analysis is performed using in-built Rasa tools in conjunction with our own testing parameters, and it is found that our system achieves a precision of 0.78, recall of 0.79 and F1-score of 0.79, which are better than the current state of the art.
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