CAERS: A Conversational Agent for Intervention in MOOCs’ Learning Processes

Rossi, Diego; Ströele, Victor; Braga, Regina; Caballé, Santi; Capuano, Nicola; Campos, Fernanda; Dantas, Mário A. R.; Lomasto, Luigi; Toti, Daniele

doi:10.1007/978-3-030-90677-1_36

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…For instance, both Kim et al [99] and Vytasek et al [105] presented an app to give feedback on the writing quality of students' essays before submission. Similarly, the conversational agent presented by Rossi et al [96] suggested learning materials to enhance students' comprehension, but if unsatisfactory, the student could ask for help from a tutor. The language tutor by Schlippe & Sawatzki [97] not only graded and gave feedback to students, but also utilised peer-review to support them, thus granting students some form of control over correct answers.…”

Section: ) Decision-makermentioning

confidence: 99%

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Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review

Pierrès,

Christen,

Schmitt-Koopmann

et al. 2024

IEEE Access

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Literature reviews on artificial intelligence (AI) have focused on the different applications of AI in higher education, the AI techniques used, and the benefits/risks of the use of AI. One of the greatest potentials of AI is to personalize higher education to the needs of students and offer timely feedback. This could benefit students with disabilities tremendously if their needs are also considered in the development of new AI educational technologies (EdTech). However, current reviews have failed to address the perspective of students with disabilities, which prompts ethical concerns. For instance, AI could treat people with disabilities as outliers in the data and end up discriminating against them. For that reason, this systematic literature review raises the following two questions: To what extent are ethical concerns considered in articles presenting AI applications assessing students (with disabilities) in higher education? What are the potential risks of using AI that assess students with disabilities in higher education? This scoping review highlights the lack of ethical reflection on AI technologies and an absence of discussion and inclusion of people with disabilities. Moreover, it identifies eight risks associated with the use of AI EdTech for students with disabilities. The review concludes with suggestions on how to mitigate these potential risks. Specifically, it advocates for increased attention to ethics within the field, the involvement of people with disabilities in research and development, as well as careful adoption of AI EdTech in higher education.

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Section: ) Decision-makermentioning

confidence: 99%

“…The language tutor by Schlippe & Sawatzki [97] not only graded and gave feedback to students, but also utilised peer-review to support them, thus granting students some form of control over correct answers. Still, in several articles, lecturers could also oversee the students' progress [44], [95], [96], [101].…”

Section: ) Decision-makermentioning

confidence: 99%

Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review

Pierrès,

Christen,

Schmitt-Koopmann

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…When a post in a discussion forum indicates that the student needs more than a message for pedagogical intervention, the agent may start and conduct an automatic interference (help) or ask the tutor for help. For automatic help, the subject in the message is recognized, and a pre-selected educational resource from the External Educational Resources repository is recommended to help the student with the content (ROSSI et al, 2021).…”

Section: Intervention Layermentioning

confidence: 99%

“…The educational domain is characterized by fragmented and specific solutions, where different organizations develop different services, tools, and plugins. Among the challenges of organizations that provide LMS are innovation, complexity, and security (Martin et al, 2018) (Rossi et al, 2021). The difficulty in maintaining LMSs with such a diversity of resources makes up an interesting scenario for Software Ecosystem (ECOS) perspective (Abdalla et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Educational Software and Security Vulnerabilities: an experimental study

Rossi,

Bressan,

Campos

et al. 2023

Anais Do XXXIV Simpósio Brasileiro De Informática Na Educação (SBIE 2023)

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The educational domain comprises fragmented solutions with different services, tools, and plugins. As a complex system, it raises several security and threat prevention concerns. We conducted an exploratory study to characterize security vulnerabilities and their impacts on Learning Management Systems. We focus on an intelligent educational solution using the risk assessment methodology HEAVENS 2.0. We identify vulnerabilities in architectural elements and detail two of them. Risks are not acceptable, and security measures must be adopted to avoid damage to students, tutors, and teachers. The results highlight the security vulnerabilities and the consequences of threats to users, hoping to motivate future research

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“…Rossi et al (83) designed a conversational agent that recommends resources to the learner according to his posts in the forum. Benjelloun Touimi et al…”

Section: Learning Elements Recommender Systemsmentioning

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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CAERS: A Conversational Agent for Intervention in MOOCs’ Learning Processes

Cited by 8 publications

References 17 publications

Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review

Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review

Educational Software and Security Vulnerabilities: an experimental study

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

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