Sequence generation for learning: a transformation from past to future

Rasheed, Fareeha; Wahid, Abdul

doi:10.1108/ijilt-01-2019-0014

Cited by 7 publications

(5 citation statements)

References 69 publications

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“…The third hypothesis shows a negative and insignificant relationship between CM and AT for MCPs (β 5 0.105; p-value < 0.1). The findings corroborate the studies of Sabah (2020), Reed (2023). The results conclude that Students' intentions to use technology for MCPs are not influenced by CM.…”

Section: Discussionsupporting

confidence: 89%

Intention to use technology for micro-credential programs: evidence from technology acceptance and self-determination model

Miao,

Ahmed,

Ahsan

et al. 2023

IJEM

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PurposeThe study aims to investigate students' attitudes toward using technology for micro-credential programs (MCPs) and their behavioral intention toward using MCPs for learning and enhancing their skills. The study also intends to investigate the moderating influence of labor market conditions (LMC).Design/methodology/approachA closed-ended questionnaire is employed to collect data from 474 participants through a convenience sample, from the university students in Karachi. Two theoretical frameworks are used in the study: technology acceptance model (TAM) and self-determination theory (SDT). The partial least squares structural equation modeling (PLS-SEM) technique is used to analyze data.FindingsFindings reveal significant and positive relationships between all variables, except controlled motivation (CM) and perceived challenges. Moreover, the moderation results ascertain that the labor market does not moderate the relationship between attitude toward using technology for MCPs and students' behavioral intention toward using MCPs for learning.Originality/valueThe study seeks to understand students' attitudes and behavioral intentions toward using technology for MCPs. Also, the moderating effect of LMC is highlighted in understanding the impact of the attitude to use technology (AT) for MCPs and behavioral intentions in higher educational institutions (HEIs) in Pakistan. The study highlights intuitive practical implications for students of HEIs, universities and digital credential program providers.

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Section: Discussionsupporting

confidence: 89%

Intention to use technology for micro-credential programs: evidence from technology acceptance and self-determination model

Miao,

Ahmed,

Ahsan

et al. 2023

IJEM

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“…Creating a learning path provides personalised learning that measures students' needs and improves learning (Rasheed & Wahid, 2019). Their study reviews learning path generation methods and evaluates the effectiveness of different methods when using e-learning materials.…”

Section: Rq 2: Methods For Recommendations and Generating Of Individu...mentioning

confidence: 99%

“…It is necessary to consider learners' changing state to construct a personalised learning path (Jiang et al, 2022;Rasheed & Wahid, 2019). Navarro and Moreno-Ger (2018) propose using Learning Analytics (LA) educational datasets in open education.…”

Section: Rq1 -Research In the Field Of Optimisation And Adaptation Of...mentioning

confidence: 99%

Generation of a Learning Path in E-Learning Environments: Literature Review

Ovtšarenko

2023

New Trends in Computer Sciences

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Education is moving into an e-learning environment, displacing contact and face-to-face learning. However, current e-learning environments cannot still personalise when creating e-learning paths. Identifying existing solutions’ problems and limitations is critical to generate new, more advanced ideas for creating personalised e-learning paths. The literature analysis, for which 28 articles for 2018–2022 were used, describes the existing solutions used to adapt and optimise e-learning. The article provides an overview of existing research in the field of personalisation of e-learning systems and the creation of e-learning trajectories, proposes the development of a taxonomy of studied methods for recommending and forming individual learning trajectories, analysis of the practices described in the articles to identify the most commonly used of them. Limitations, problems and unresolved issues in previous studies are summarised and provide information for further work on improving the results obtained and for choosing the direction of future research, which is given in the final part of the article.

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“…Figure 2 illustrates the research methodology; this methodology is revised from Rasheed and Wahid [16], and Akbar and Irohara [17], as they suggest reasonable and logical procedures for literature review and analysis. In the first step, the purpose and scope of the review are defined, as described in Section 2.2.…”

Section: Methodsmentioning

confidence: 99%

“…Meanwhile, the product sequence concentrates on the sequence of products when a specific product enters a machine. As shown in Table 3, 16 of the studies of interest used the resource sequence, 15 used the job sequence, 16 combined resource and job sequences, and no studies used the product sequence as their main sequence type.…”

Section: Sequence Typementioning

confidence: 99%

A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

Meilanitasari

Shin

2021

Processes

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This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summarized as follows: less consideration of uncertainty factors causes a gap between the reality and the derived schedules; the use of stationary dispatching rules is limited to reflect the dynamics and flexibility; production-level scheduling is restricted to increase responsiveness owing to product-level uncertainty; and optimization is more focused, while prediction is used mostly for verification and validation, although prediction-then-optimization is the standard stream in data analytics. In future research, the degree of uncertainty should be quantified and modeled explicitly; both holistic and granular algorithms should be considered; product sequences should be incorporated; and sequence learning should be applied to implement the prediction-then-optimization stream. This would enable us to derive data-learned prediction and optimization models that output accurate and precise schedules; foresee individual product locations; and respond rapidly to dynamic and frequent changes in JS-FMSs.

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Sequence generation for learning: a transformation from past to future

Cited by 7 publications

References 69 publications

Intention to use technology for micro-credential programs: evidence from technology acceptance and self-determination model

Intention to use technology for micro-credential programs: evidence from technology acceptance and self-determination model

Generation of a Learning Path in E-Learning Environments: Literature Review

A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

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