Charting Futures: A Comprehensive Review of Guided Pathways in Undergraduate Programs for Career Selection using Machine Learning
Keywords:
Career congruence, Career guidance, Career Selection, Decision-making, Machine LearningAbstract
for unintentional deviations from the prescribed paths to completion. Additionally, success is enhanced when these programs have fewer bureaucratic obstacles, facilitating a smoother journey for students without unnecessary circumnavigation efforts. Resilience, within the realm of learning, is a complex concept encompassing an individual's ability to both generate and optimize opportunities while also responding positively to setbacks and challenges. The cultivation of students' resilience is gaining significance, supported by research demonstrating its connections to achievement, retention, engagement, and employability. Despite this recognition, there is a paucity of research investigating the specific elements of curricula that contribute to resilience and, more specifically, the distinctive role played by active learning frameworks in attaining this objective. This research presents a review that aids in comprehending the diverse facets of structure within community college career and technical programs. This paper serves as a valuable resource for practitioners engaged in program and policy design. Furthermore, it offers researchers a tool to gauge the adoption and effects of approaches characterized by a relatively higher degree of structure.
Downloads
References
Mayor-Silva, L. I., Del Gallego-Lastra, R., Meneses-Monroy, A., Hernández-Martín, M. M., Álvarez-Méndez, A. M., Romero-Saldaña, M., & Moreno, G. (2024). A scale for assessing nursing students' emotional competence: A validation study. Nurse Education Today, 133, 106046.
Cook, K. S., Raghavan, M., & Fogelberg, K. (2024). Assessing Student Learning: Exams, Quizzes, and Remediation. Educational Principles and Practice in Veterinary Medicine, 287.
Blalock, K., Chan, F., Cardoso, E., Lee, E. J., Iwanaga, K., Wu, J. R., & Chen, X. (2024). Psychometric validation of the scale of ethnocultural empathy in a sample of undergraduate and graduate students in rehabilitation: A brief report. Rehabilitation Counseling Bulletin, 67(2), 131-138.
Rad, H. S., & Alipour, R. (2024). Unlocking writing success: Building assessment literacy for students and teachers through effective interventions. Assessing Writing, 59, 100804.
Li, J., Luo, Z., Chen, Y., Zhao, Q., Zhang, S., Zhou, W., ... & Tang, J. (2024). Measuring care-related regret intensity: Reliability and validation of a 10-item scale among Chinese nursing interns. Nurse Education in Practice, 74, 103845.
Rostami, Z., Abedi, M. R., & Nilforooshan, P. (2024). Developing and Examining the Psychometric Properties of Career Issues Inventory for Bipolar Disorder (CII-BD): An Instrument for Early Vocational Rehabilitation. Journal of Health and Safety at Work, 13(4), 754-778.
Bailey, M. R. (2024). Documenting Teaching for Career Advancement. Educational Principles and Practice in Veterinary Medicine, 399.
Reubenson, A., Ng, L., & Gucciardi, D. F. (2020). The Assessment of Physiotherapy Practice tool provides informative assessments of clinical and professional dimensions of student performance in undergraduate placements: a longitudinal validity and reliability study. Journal of Physiotherapy, 66(2), 113-119.
Øvrebø, L. J., Dyrstad, D. N., & Hansen, B. S. (2022). Assessment methods and tools to evaluate postgraduate critical care nursing students' competence in clinical placement. An integrative review. Nurse Education in Practice, 58, 103258.
Kavanagh, A., Prentice, G., Donohue, G., Doyle, C., Farrington, A., Jennings, B., & King, M. (2022). Development of the Student Nurse Subjective Evaluation of Completed Clinical Practice Placement Instrument. Journal of Nursing Education, 61(10), 559-569.
Albloushi, M., Ferguson, L., Hellsten, L., Stemler, L., Kent-Wilkinson, A., & Bassendowski, S. (2022). Modification, translation, and psychometric testing of the Arabic version of the Belongingness Scale-Clinical Placement Experience. Journal of Nursing Measurement, 30(1), 94-108.
Lunde, G. H., Blaalid, L., Gerbild, H., & Josefsson, K. A. (2022). Assessment of the psychometrics of the Students' Attitudes towards Addressing Sexual Health Extended (SA-SH-Ext) questionnaire for social educator students. Sexual Medicine, 10(3), 100507.
Pueyo-Garrigues, M., Agüera, Z., Andrés, A., Lluch-Canut, M. T., Tricas-Sauras, S., Duaso, M. J., ... & INSTrUCT research group. (2023). Knowledge, attitudes, behavioral and organizational factors of health professions students for a competent smoking cessation practice: An instrument adaptation and psychometric validation study in Spanish and English samples. Nurse Education in Practice, 70, 103647.
Yang, L., Pang, F., & Sin, K. F. (2023). Assessing the Psychometric Properties of the Practice and Product Inventory of Supporting Students with ASD (PPI-SSA): A Concise Assessment Tool for Teachers in Inclusive Classrooms. Sustainability, 15(19), 14576.
Judd, B., Brentnall, J., Scanlan, J. N., Thomson, K., Blackstock, F., Mandrusiak, A., ... & McAllister, S. (2023). Evaluating allied health students’ readiness for placement learning. BMC Medical Education, 23(1), 1-12.
Alidosti, M., Shahnazi, H., Heidari, Z., & Zamani-Alavijeh, F. (2022). Development and psychometric assessment of cutaneous leishmaniasis prevention behaviors questionnaire in adolescent female students: Application of integration of cultural model and extended parallel process model. Plos one, 17(8), e0273400.
Chen, M., Zee, M., & Roorda, D. L. (2022). Assessing student–teacher relationship quality in cross-cultural contexts: Psychometric properties of student–teacher relationship drawings. European Journal of Developmental Psychology, 19(5), 770-784.
Christopher, K., & Lord, C. (2022). Best practice assessments for autism spectrum disorders in schools. Psychology in the Schools, 59(7), 1454-1468.
Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., bin Saleh, K., ... & Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy.
Gu, J., Wang, J., Guo, X., Liu, G., Qin, S., & Bi, Z. (2023). A metaverse-based teaching building evacuation training system with deep reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(4), 2209-2219.
Sefora, S., & Ngubane, S. A. (2023). Career development for students with disabilities in an open distance learning institution: A narrative inquiry. Disability & society, 38(3), 445-459.
Mahat, M., Dollinger, M., D’Angelo, B., Naylor, R., & Harvey, A. (2023). Co-designing a curriculum model for career education: perspectives from regional communities in Australia. The Australian Educational Researcher, 50(2), 409-431.
Fantinelli, S., Esposito, C., Carlucci, L., Limone, P., & Sulla, F. (2023). The influence of individual and contextual factors on the vocational choices of adolescents and their impact on well-being. Behavioral Sciences, 13(3), 233.
Makkonen, T., Lavonen, J., & Tirri, K. (2023). Actualizing talent in physics: A qualitative study of gifted Finnish upper-secondary-school physics students. Journal for the Education of the Gifted, 46(1), 3-33.
Guleria, P., & Sood, M. (2023). Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling. Education and Information Technologies, 28(1), 1081-1116.
Liu, L. T., Wang, S., Britton, T., & Abebe, R. (2023). Reimagining the machine learning life cycle to improve educational outcomes of students. Proceedings of the National Academy of Sciences, 120(9), e2204781120.
Gossen, D., & Ivey, T. (2023). The Impact of In-and Out-of-School Learning Experiences in the Development of Students’ STEM Self-Efficacies and Career Intentions. Journal for STEM Education Research, 6(1), 45-74.
Kirdani-Ryan, M., Ko, A. J., & Borisova, E. A. (2023). “Taught to be automata”: Examining the departmental role inshaping initial career choices of computing students. Computer Science Education, 1-27.
Alghamdi, A. A. (2023). A novel intelligent agent-based framework for appropriate stream selection from perceptive of career counseling. PeerJ Computer Science, 9, e1256.
George, B., & Wooden, O. (2023). Managing the strategic transformation of higher education through artificial intelligence. Administrative Sciences, 13(9), 196.
Narayan, Vipul, et al. "7 Extracting business methodology: using artificial intelligence-based method." Semantic Intelligent Computing and Applications 16 (2023): 123
Narayan, Vipul, et al. "A Comprehensive Review of Various Approach for Medical Image Segmentation and Disease Prediction." Wireless Personal Communications 132.3 (2023): 1819-1848.
Mall, Pawan Kumar, et al. "Rank Based Two Stage Semi-Supervised Deep Learning Model for X-Ray Images Classification: AN APPROACH TOWARD TAGGING UNLABELED MEDICAL DATASET." Journal of Scientific & Industrial Research (JSIR) 82.08 (2023): 818-830.
Narayan, Vipul, et al. "Severity of Lumpy Disease detection based on Deep Learning Technique." 2023 International Conference on Disruptive Technologies (ICDT). IEEE, 2023.
Saxena, Aditya, et al. "Comparative Analysis Of AI Regression And Classification Models For Predicting House Damages İn Nepal: Proposed Architectures And Techniques." Journal of Pharmaceutical Negative Results (2022): 6203-6215.
Kumar, Vaibhav, et al. "A Machine Learning Approach For Predicting Onset And Progression"“Towards Early Detection Of Chronic Diseases “." Journal of Pharmaceutical Negative Results (2022): 6195-6202.
Chaturvedi, Pooja, Ajai Kumar Daniel, and Vipul Narayan. "Coverage Prediction for Target Coverage in WSN Using Machine Learning Approaches." (2021).
Chaturvedi, Pooja, A. K. Daniel, and Vipul Narayan. "A Novel Heuristic for Maximizing Lifetime of Target Coverage in Wireless Sensor Networks." Advanced Wireless Communication and Sensor Networks. Chapman and Hall/CRC 227-242.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
