TutorBot: Contextual Learning Guide for Software Engineers

Subramanian, V.; Ramachandra, Nisha; Dubash, Neville

doi:10.1109/botse.2019.00011

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…Besides being applied for information retrieval solutions, bots are also employed in learning media. TutorBot ( Subramanian, Ramachandra & Dubash, 2019 ) helps software developers to search for technical content from Stack Overflow, GitHub, and Massive Open Online Courses (MOOCs) in a single place. The bot is built as a conversational UI (with voice support), which also attaches a recommendation engine for suggesting relevant topics as per collaborative filtering and content analysis.…”

Section: Resultsmentioning

confidence: 99%

Bots in software engineering: a systematic mapping study

Santhanam

Hecking

Schreiber

et al. 2022

PeerJ Computer Science

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Bots have emerged from research prototypes to deployable systems due to the recent developments in machine learning, natural language processing and understanding techniques. In software engineering, bots range from simple automated scripts to decision-making autonomous systems. The spectrum of applications of bots in software engineering is so wide and diverse, that a comprehensive overview and categorization of such bots is needed. Existing works considered selective bots to be analyzed and failed to provide the overall picture. Hence it is significant to categorize bots in software engineering through analyzing why, what and how the bots are applied in software engineering. We approach the problem with a systematic mapping study based on the research articles published in this topic. This study focuses on classification of bots used in software engineering, the various dimensions of the characteristics, the more frequently researched area, potential research spaces to be explored and the perception of bots in the developer community. This study aims to provide an introduction and a broad overview of bots used in software engineering. Discussions of the feedback and results from several studies provide interesting insights and prospective future directions.

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Section: Resultsmentioning

confidence: 99%

Bots in software engineering: a systematic mapping study

Santhanam

Hecking

Schreiber

et al. 2022

PeerJ Computer Science

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“…• Learning Element Recommender: Subramanian et al (161) has not clearly mentioned any preprocessing technique.…”

Section: Othersmentioning

confidence: 99%

“…They used topic modeling for the modeling of videos and sequential pattern mining to find relationships between different topics of the videos and then used them to rec-ommend related videos to learners in sequence, and used CBF for their RS design. Subramanian et al (161) designed a chatbot for software engineers that uses natural language processing and content matching to recommend related resources to users. Liu and Li (163) designed a recommender system that maintains course and learner networks and establishes relationships between different learners and courses based on their history in those courses, i.e., two courses will be connected if they are registered by the same user.…”

Section: Learning Element Recommendermentioning

confidence: 99%

Scalable and Practical Recommender System in Massive Open Online Courses (MOOCs)

Khalid

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Massive Open Online Courses (MOOCs) have witnessed a surge in popularity among learners and providers, prompting researchers to explore the enhancement of MOOC utilisation through recommendation systems. However, while existing efforts primarily focus on designing recommender systems capable of providing recommendations across diverse areas of MOOCs, less attention is given to essential characteristics of a robust recommender system, such as practicality and scalability. This literature gap leads to a lack of practical and scalable recommender systems capable of effectively handling increasing volumes of data. Furthermore, current systems leveraging neural networks and deep learning for user behavioural data-based recommendations face challenges in accommodating new data points without retraining. Consequently, scalable and practical recommendation algorithms for MOOCs remain limited in the current literature.To address these shortcomings, this research aims to contribute to the development of scalable and practical recommender systems for MOOCs by utilising demographic and behavioural data of learners, alongside the unique characteristics of MOOCs, for personalised recommendations. In line with these objectives, the study introduced the Novel online Recommender system for MOOCs (NoR-MOOCs) specifically designed for course recommendation. Preliminary studies on the COCO dataset demonstrate the algorithm's promising performance. NoR-MOOCs constructs user profiles based on course ratings provided by users, and its performance evaluation employs predictive and classification accuracy metrics (RMSE, ROC, precision, recall and coverage). A comparison with traditional K-Means and collaborative Filtering algorithms validates the effectiveness of NoR-MOOCs.Additionally, considering the abundant user behavioural and demographic data available in MOOCs, a Novel online Multi-attribute-based Recommendation algorithm for MOOCs (NoM-MOOCs) is designed. This algorithm creates user profiles based on their behavioural and demographic characteristics. Extensive experiments on the Edx and CAN datasets demonstrate the algorithm's significant out-performance compared to the Kernal Mapping recommender system (KMR) in terms of predictive and classification accuracy metrics. Moreover, the analysis of behavioural data from MOOCs logs contributes to forming valuable learning pathways for learners. Existing literature has extensively explored the design and implementation of learning pathway recommendation systems, with such recommender systems being intricately designed using neural networks and deep learning. To this end, a fast online learning path recommender system that incorporates demographic and behavioural data of learners to recommend learning paths is designed. This system identifies similar learners by leveraging demographics and behavioural data and recommends pats paths taken by successful students to struggling students with similar behavioural and demographic profiles. To evaluate the results of this proposed algorithm and the potential impact of the learning path recommender system on learners, an interview with the MOOC professor is conducted.Overall, this research strives to contribute to the advancement of scalable and practical recommender systems for MOOCs, enriching the learning experiences of users and promoting the effective utilisation of MOOCs platforms.

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