Automated Adaptive Mobile Learning System using Shortest Path Algorithm and Learning Style

Alshalabi, Ibrahim Alkore; Hamada, Samir; Elleithy, Khaled; Badara, Ioana A.; Moslehpour, Saeid

doi:10.3991/ijim.v12i5.8186

Cited by 14 publications

(9 citation statements)

References 16 publications

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“…To describe the structure of domain knowledge, including the learning goals, and all other available learning paths, this study has proposed a learning path graph (LPG) inscribed in a system prototype that implements adaptive learning path algorithms. The proposed algorithms use students' information from their profiles and their learning style to improve academic performance within an m-learning system that provides a suitable course content sequence in a personalized manner [32]. Many investigations are devoted to analyzing the capabilities of mobile applications that can advance learning and develop students' professional skills.…”

Section: Discussionmentioning

confidence: 99%

Integration of Mobile Learning into Complex Problem-Solving Processes during STEM Education

Shchedrina

Galkina

Petunina

et al. 2020

Int. J. Interact. Mob. Technol.

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<p class="0abstract">Over the past few years, the teaching process has transformed radically under significant investments in information and communication technologies. In this context, mobile technologies emerge as an innovative educational tool. Mobile devices are being used by a vast number of so-called "digital generation" representatives in daily life and educational activities. It has been proven that the implementation of mobile technologies in education contributes to the increase of students' motivation, participation in the learning process, and faster acquisition of professional competencies. These technologies take the role of the "driving force" in training. However, their comprehensive understanding is essential to use them effectively. The objective of this study was to analyze the intensity of mobile technologies' use and investigate their evolution in higher education using the example of the I.M. Sechenov First Moscow State Medical University. The research sample was represented by 151 students (data collected for 2015/2016) and 274 students (data for 2019/2020). The average participants' age corresponded to 19.8. The study also involved three experts who were required to evaluate questionnaires completed in Google Forms. The scientific method of the study was based on the organized examination, strict control over the involved respondents, and quantitative research. The research outcomes were analyzed through Chi-Square goodness-of-fit test. The target questions of the survey were rated on a 5-point Likert scale. According to the study results, 95% of the respondents used mobile devices for educational purposes, of which 65% agreed with the convenience of having course materials on a mobile device. In the 2015/2016 academic year, the share of students using smartphones for learning comprised 10.4%, while in 2019/2020, their percentage increased to 61.5%. The study findings will be useful for university teachers and representatives of educational institutions' administration.</p>

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Section: Discussionmentioning

confidence: 99%

Integration of Mobile Learning into Complex Problem-Solving Processes during STEM Education

Shchedrina

Galkina

Petunina

et al. 2020

Int. J. Interact. Mob. Technol.

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“…[34] P22 [35] them from the rest of the references in this document, we assigned them an identifier, as seen in Table 2. Concerning the years of publication of the selected studies, a variable frequency can be observed regarding the number of publications, highlighting the years 2012, 2015 2017, and 2019, as seen in Figure 2.…”

Section: P1 [14] P2mentioning

confidence: 99%

Software Architectures for Adaptive Mobile Learning Systems: A Systematic Literature Review

Ruiz Nepomuceno,

López Domínguez,

Domínguez Isidro

et al. 2024

Applied Sciences

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Adaptive Mobile Learning Systems (AMLSs) are technological tools that incorporate adaptive learning to generate an individual teaching–learning process for each student. Despite the proliferation of AMLS, their development is often hindered by a lack of standardization and the poor quality of existing software architectures in terms of requirements correctness and coverage. This paper presents a systematic literature review (SLR) of software architectures oriented to developing AMLS reported in the state of the art. For this SLR, we searched the ACM Digital Library, IEEE Xplore, Science Direct, Springer, and SCOPUS. Studies published in English that describe and evaluate a software architecture to develop AMLS were included. Twenty-two primary works were relevant to the present study. Based on the obtained results, we have identified key aspects that must be considered in a software architecture proposed for developing an AMLS in terms of requirements, quality attributes, stakeholders, services provided to users, views and architectural styles, components, and implementation technologies. The main finding of this work is the lack of a reference software architecture that considers all the requirements and quality attributes needed for developing AMLS. This gap hinders the effective development and standardization of quality AMLSs, suggesting a critical need for a comprehensive reference software architecture.

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“…Furthermore, Karas et al [11] proposed a method to help students to form the knowledge network model and analysis of KG. In another study, Alshalabi et al [12] proposed an adaptive learning path algorithm by analyzing the structure of domain-specific knowledge to improve the academic performance of students. Moreover, Lv et al [13] solved the lack of logic knowledge relations on the semantic level using the nursing KG of the curriculum model.…”

Section: State Of the Artmentioning

confidence: 99%

Opening Knowledge Graph Model Building of Artificial Intelligence Curriculum

Yue

Lin

Jin

et al. 2022

Int. J. Emerg. Technol. Learn.

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The knowledge points setting of artificial intelligence curriculum has shortcomings in connection between theory and practices. To overcome the problem, this study designs an open knowledge point design model based on knowledge graph. Fist, to promote the construction of the knowledge graph (KG) of curriculums, associated teaching research was analyzed visually. Then the order and hierarchical structure of the knowledge points were defined, and the ontology structure of curriculum knowledge and the relationship between knowledge points and posts were designed as well. Moreover, an overall logic structure for the construction of the open KG of curriculums was proposed. Results demonstrated that high attention should be paid to the construction and concern of teaching teams for artificial intelligence algorithms and the KG of curriculum construction. Additionally, the opening model can strengthen the openness of the KG of curriculums to reinforce the close connections between classroom knowledge and practices. Research conclusions are conducive to understand the existing problems in the KG of curriculums and provide beneficial references to the integration of information technology and education.

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Automated Adaptive Mobile Learning System using Shortest Path Algorithm and Learning Style

Cited by 14 publications

References 16 publications

Integration of Mobile Learning into Complex Problem-Solving Processes during STEM Education

Integration of Mobile Learning into Complex Problem-Solving Processes during STEM Education

Software Architectures for Adaptive Mobile Learning Systems: A Systematic Literature Review

Opening Knowledge Graph Model Building of Artificial Intelligence Curriculum

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