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- ItemAn AI-based open recommender system for personalized labor market driven education(Amsterdam [u.a.] : Elsevier Science, 2022) Tavakoli, Mohammadreza; Faraji, Abdolali; Vrolijk, Jarno; Molavi, Mohammadreza; Mol, Stefan T.; Kismihók, GáborAttaining those skills that match labor market demand is getting increasingly complicated, not in the last place in engineering education, as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Anticipating and addressing such dynamism is a fundamental challenge to twenty-first century education. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. In this paper, we propose a novel, Artificial Intelligence (AI) driven approach to the development of an open, personalized, and labor market oriented learning recommender system, called eDoer. We discuss the complete system development cycle starting with a systematic user requirements gathering, and followed by system design, implementation, and validation. Our recommender prototype (1) derives the skill requirements for particular occupations through an analysis of online job vacancy announcements
- ItemA Recommender System For Open Educational Videos Based On Skill Requirements(Ithaca, NY : Cornell University, 2020) Tavakoli, Mohammadreza; Hakimov, Sherzod; Ewerth, Ralph; Kismihók, GáborIn this paper, we suggest a novel method to help learners find relevant open educational videos to master skills demanded on the labour market. We have built a prototype, which 1) applies text classification and text mining methods on job vacancy announcements to match jobs and their required skills; 2) predicts the quality of videos; and 3) creates an open educational video recommender system to suggest personalized learning content to learners. For the first evaluation of this prototype we focused on the area of data science related jobs. Our prototype was evaluated by in-depth, semi-structured interviews. 15 subject matter experts provided feedback to assess how our recommender prototype performs in terms of its objectives, logic, and contribution to learning. More than 250 videos were recommended, and 82.8% of these recommendations were treated as useful by the interviewees. Moreover, interviews revealed that our personalized video recommender system, has the potential to improve the learning experience.
- ItemExtracting Topics from Open Educational Resources(Ithaca, NY : Cornell University, 2020) Molavi, Mohammadreza; Tavakoli, Mohammadreza; Kismihók, GáborIn recent years, Open Educational Resources (OERs) were earmarked as critical when mitigating the increasing need for education globally. Obviously, OERs have high-potential to satisfy learners in many different circumstances, as they are available in a wide range of contexts. However, the low-quality of OER metadata, in general, is one of the main reasons behind the lack of personalised services such as search and recommendation. As a result, the applicability of OERs remains limited. Nevertheless, OER metadata about covered topics (subjects) is essentially required by learners to build effective learning pathways towards their individual learning objectives. Therefore, in this paper, we report on a work in progress project proposing an OER topic extraction approach, applying text mining techniques, to generate high-quality OER metadata about topic distribution. This is done by: 1) collecting 123 lectures from Coursera and Khan Academy in the area of data science related skills, 2) applying Latent Dirichlet Allocation (LDA) on the collected resources in order to extract existing topics related to these skills, and 3) defining topic distributions covered by a particular OER. To evaluate our model, we used the data-set of educational resources from Youtube, and compared our topic distribution results with their manually defined target topics with the help of 3 experts in the area of data science. As a result, our model extracted topics with 79% of F1-score.
- ItemA multi-method psychometric assessment of the affinity for technology interaction (ATI) scale(Amsterdam : Elsevier, 2020) Lezhnina, Olga; Kismihók, GáborIn order to develop valid and reliable instruments, psychometric validation should be conducted as an iterative process that “requires a multi-method assessment” (Schimmack, 2019, p. 4). In this study, a multi-method psychometric approach was applied to a recently developed and validated scale, the Affinity for Technology Interaction (ATI) scale (Franke, Attig, & Wessel, 2018). The dataset (N = 240) shared by the authors of the scale (Franke et al., 2018) was used. Construct validity of the ATI was explored by means of hierarchical clustering on variables, and its psychometric properties were analysed in accordance with an extended psychometric protocol (Dima, 2018) by methods of Classical Test Theory (CTT) and Item Response Theory (IRT). The results showed that the ATI is a unidimensional scale (homogeneity H = 0.55) with excellent reliability (ω = 0.90 [0.88-0.92]) and construct validity. Suggestions for further improvement of the ATI scale and the psychometric protocol were made.
- ItemMetadata analysis of open educational resources(New York,NY,United States : Association for Computing Machinery, 2021) Tavakoli, Mohammadreza; Elias, Mirette; Kismihók, Gábor; Auer, Sören; Scheffel, MarenOpen Educational Resources (OERs) are openly licensed educational materials that are widely used for learning. Nowadays, many online learning repositories provide millions of OERs. Therefore, it is exceedingly difficult for learners to find the most appropriate OER among these resources. Subsequently, the precise OER metadata is critical for providing high-quality services such as search and recommendation. Moreover, metadata facilitates the process of automatic OER quality control as the continuously increasing number of OERs makes manual quality control extremely difficult. This work uses the metadata of 8,887 OERs to perform an exploratory data analysis on OER metadata. Accordingly, this work proposes metadata-based scoring and prediction models to anticipate the quality of OERs. Based on the results, our analysis demonstrated that OER metadata and OER content qualities are closely related, as we could detect high-quality OERs with an accuracy of 94.6%. Our model was also evaluated on 884 educational videos from Youtube to show its applicability on other educational repositories.