Modelling Math Learning on an Open Access Intelligent Tutor

Azcona, David; Hsiao, I-Han; Smeaton, Alan F.

doi:10.1007/978-3-319-93846-2_7

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Azcona [3] reported the implementation of a variety of algorithms, based on Machine Learning and Deep Learning as teaching support tools in several programming courses. Azcona's objective was to recommend programs' source code to students.…”

Section: Recommendation Systems Based On Collective Knowledgementioning

confidence: 99%

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Improving learning experiences in software engineering capstone courses using artificial intelligence virtual assistants

Gonzalez

Neyem

Contreras‐McKay

et al. 2022

Comp Applic In Engineering

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Students of Software Engineering Capstone Courses face situations and challenges that grant them valuable experiences. However, as this knowledge is acquired through real-life exposure, it is difficult to transmit it across different generations. To deal with this problem, it has been proposed that students record their learnings through a lesson learned model. But the question of how future students can best benefit from these records remains unanswered. This study addresses this problem through the use of Artificial Intelligence (AI) Virtual Assistant combined with a recommender system. Artificial Intelligence Virtual Assistants, as conversational chatbots, are a manifestation of AI via the simulation of conversation with human users. The technology has the potential to provide personalized service to a range of stakeholders. Thus, we seek to move this trend forward by now pushing these capabilities into the field of higher education. Consequently, we aim to assist software engineering students by leveraging collective knowledge to enhance learning experiences.

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Section: Recommendation Systems Based On Collective Knowledgementioning

confidence: 99%

“…To apply Student's t test, we calculate the estimated standard error of the difference between samples means (SED) as indicated in formula (1). The t-statistic is calculated with formula (2), and the degrees of freedom as indicated in formula (3). The obtaining results are shown in Table 8.…”

Section: Serendipitymentioning

confidence: 99%

Improving learning experiences in software engineering capstone courses using artificial intelligence virtual assistants

Gonzalez

Neyem

Contreras‐McKay

et al. 2022

Comp Applic In Engineering

View full text Add to dashboard Cite

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“…The most important feature of ITSs is their ability to customize instructional strategies and activities according to learner's requirements and characteristics [9,10]. Many researchers contribute their efforts for refining student's ability to solve different problems in education [11][12][13][14]. The first ITS named SCHOLAR tutor was introduced in 1970 [15].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Domains, Techniques, Delivery Modes and Validation Methods for Intelligent Tutoring Systems

Soofi¹,

Ahmed²

2019

ijacsa

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An Intelligent Tutoring System (ITS) is a computer software that help students in learning educational or academics concepts in customized environment. ITSs are instructional systems that have capability to facilitate user by providing instantaneous feedback and instructions without any human intervention. The advancement of new technologies has integrated computer based learning with artificial intelligence methods with aim to develop better custom-made education systems that referred as ITS. One of the important factors that affect students learning process is self-learning; all students cannot have similar experience of learning scholastic concepts from same educational material. Because students have individual differences that make some topics difficult or easy to understand regarding taken subjects. These systems have capability to improve teaching and learning process in different educational domains while respecting individual learning needs. In this study an attempt is made to review the research in field of ITSs and highlight the educational areas or domains in which ITSs have been introduced. Techniques, delivering modes and evaluation methodologies that have been used in developed ITSs have also been discussed in this work. This work will be helpful for both academia and new comers in the field of ITSs to further strengthen basis of tutoring systems in educational domains.

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