Developing a quality assessment model (QAM) using logical prediction: Binary validation

Year 2024, Volume: 11 Issue: 2, 288 - 302

Sameer Mohammed Majed Dandan Odai Falah Mohammad Al-ghaswyneh

https://doi.org/10.21449/ijate.1353393

Abstract

This study focuses on evaluating the quality of competency transfer through various assessment methods and results, considering diverse stakeholder perspectives. The research aims to introduce an innovative approach for validating assessment outcomes, leveraging predicted sub-measurements, and transforming Boolean parameters' symbols into a binary coding system. This transformation simplifies the validation process by employing logical equations. The study's sample involves the adaptation of a competency transfer model, which combines internal parameters with the novel logical assessment method. The research findings indicate that the binary 2x system effectively simplifies quantitative and qualitative data representation within the validation process. This system facilitates the early detection of potentially ambiguous results, enabling the creation of validation procedures grounded in organizational cultural dimensions, outcomes, reports, and assessments. The proposed Quality Assessment Model (QAM) serves as a powerful tool for prediction, enhancing the quality of both quantitative and qualitative data outcomes. This approach generates distinct values, precise predictive measurements, and valuable result quality suitable for informed decision-making in various contexts. Ultimately, the study contributes to the advancement of assessment methodologies, enabling stakeholders to make more accurate and reliable judgments based on the quality of competency transfer.

Keywords

Assessment, Binary system, Competencies transfer, Prediction, Quality, Boolean

Supporting Institution

The support of this research study by the grant no: (BSAA-2023-12-2296) from the Deanship of Scientific Research in Northern Border University.

Project Number

BSAA-2023-12-2296

References

• Alam, S.M.T. (2015). Factors affecting job satisfaction, motivation and turnover rate of medical promotion officer (MPO) in pharmaceutical industry: a study based in Khulna city. Asian Business Review, 1(2), 126-131.
• Alas, R., Gao, J., & Carneiro, J. (2015). Connections between ethics and cultural dimensions. Engineering Economics, 21(3).
• Alnasib, B.N. (2023). Digital Competencies: Are Pre-Service Teachers Qualified for Digital Education? International Journal of Education in Mathematics, Science and Technology, 11(1), 96-114.
• Arnold, J.G., Moriasi, D.N., Gassman, P.W., Abbaspour, K.C., White, M.J., Srinivasan, R., . . . Van Liew, M.W. (2012). SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1491-1508.
• Baqais, A.A.B., & Alshayeb, M. (2020). Automatic software refactoring: A systematic literature review. Software Quality Journal, 28(2), 459-502.
• Boole, G. (1854). An investigation of the laws of thought: on which are founded the mathematical theories of logic and probabilities. Dover Publications.
• Brandt, C., & Dimmitt, N. (2015). Transfer of learning in the development of peer tutor competence. Learning and Teaching in Higher Education: Gulf Perspectives, 12(2).
• Bratianu, C., Hadad, S., & Bejinaru, R. (2020). Paradigm shift in business education: a competence-based approach. Sustainability, 12(4), 1348.
• Bride, H., Cai, C.-H., Dong, J., Dong, J.S., Hóu, Z., Mirjalili, S., & Sun, J. (2021). Silas: A high-performance machine learning foundation for logical reasoning and verification. Expert Systems with Applications, 176, 114806.
• Burggräf, P., Wagner, J., Heinbach, B., Steinberg, F., Schmallenbach, L., Garcke, J., . . . Wolter, M. (2021). Predictive analytics in quality assurance for assembly processes: Lessons learned from a case study at an industry 4.0 demonstration cell. Procedia CIRP, 104, 641-646.
• Cetiner, M., & Sahingoz, O.K. (2020). A comparative analysis for machine learning based software defect prediction systems. 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT).
• Chen, J., & Siu, S. W. (2020). Machine learning approaches for quality assessment of protein structures. Biomolecules, 10(4), 626.
• Croft, R., Babar, M.A., & Kholoosi, M.M. (2023). Data quality for software vulnerability datasets. IEEE/ACM 45th International Conference on Software Engineering (ICSE).
• Dami, S., Barforoush, A.A., & Shirazi, H. (2018). News events prediction using Markov logic networks. Journal of Information Science, 44(1), 91-109.
• Dandan, S.M. (2017). Stakeholder Satisfication with Competencies Transfer in the Framework of Educational Policy Elements [Book, Faculty of Organisation Studies, Ljubljana]. FOS Novi trg 5, 8000 Novo mesto.
• Dias, J.M.P., Oliveira, C.M., & da Silva Cruz, L.A. (2014). Retinal image quality assessment using generic image quality indicators. Information Fusion, 19, 73-90.
• East, R., East, R., Uncles, M.D., Uncles, M.D., Romaniuk, J., Romaniuk, J., . . . Lomax, W. (2016). Validation and sufficiency. European Journal of Marketing, 50(3/4), 661-666.
• Evans, R., Saxton, D., Amos, D., Kohli, P., & Grefenstette, E. (2018). Can Neural Networks Understand Logical Entailment? arXiv preprint arXiv:1802.08535.
• Fayaz, M., Ullah, I., & Kim, D.-H. (2018). Underground risk index assessment and prediction using a simplified hierarchical fuzzy logic model and kalman filter. Processes, 6(8), 103.
• Fox, D.G. (1981). Judging air quality model performance. Bulletin of the American Meteorological Society, 62(5), 599-609.
• Göckede, M., Rebmann, C., & Foken, T. (2004). A combination of quality assessment tools for eddy covariance measurements with footprint modelling for the characterisation of complex sites. Agricultural and Forest Meteorology, 127(3), 175-188.
• Graymore, M.L., Sipe, N.G., & Rickson, R.E. (2008). Regional sustainability: How useful are current tools of sustainability assessment at the regional scale?. Ecological Economics, 67(3), 362-372.
• Grönroos, C. (1984). A service quality model and its marketing implications. European Journal of Marketing, 18(4), 36-44.
• Guion, L.A. (2002). Triangulation: Establishing the validity of qualitative studies. University of Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, EDIS.
• Gutierrez Gutierrez, L., Barrales-Molina, V., & Tamayo-Torres, J. (2016). The knowledge transfer process in Six Sigma subsidiary firms. Total Quality Management & Business Excellence, 27(5-6), 613-627.
• Hawthorne, G., Saggar, M., Quintin, E.-M., Bott, N., Keinitz, E., Liu, N., . . . Reiss, A.L. (2016). Designing a creativity assessment tool for the twenty-first century: Preliminary Results and insights from developing a design-thinking based assessment of creative capacity. In Design Thinking Research (pp. 111-123). Springer.
• Hossain, M. (2015). Dimensions of satisfaction factors: Road to successful & sustainable organization. IOSR Journal of Business and Management, 17(8), 94-106.
• Hranisavljevic, N., Niggemann, O., & Maier, A. (2020). A novel anomaly detection algorithm for hybrid production systems based on deep learning and timed automata. arXiv preprint arXiv:2010.15415.
• Jabangwe, R., Börstler, J., Šmite, D., & Wohlin, C. (2015). Empirical evidence on the link between object-oriented measures and external quality attributes: a systematic literature review. Empirical Software Engineering, 20(3), 640-693.
• Jafarian, T., Masdari, M., Ghaffari, A., & Majidzadeh, K. (2020). Security anomaly detection in software‐defined networking based on a prediction technique. International Journal of Communication Systems, 33(14), e4524.
• Klein, S.M., & Maher, J. (1966). Education level and satisfaction with pay. Personnel Psychology, 19(2), 195-208.
• Koskinen, K.U., & Pihlanto, P. (2006). Competence transfer from old timers to newcomers analysed with the help of the holistic concept of man. Knowledge and Process Management, 13(1), 3-12.
• Li, F., & Yu, F. (2020). Multi-factor one-order cross-association fuzzy logical relationships based forecasting models of time series. Information Sciences, 508, 309-328.
• Marchisio, M., Barana, A., Fioravera, M., Rabellino, S., & Conte, A. (2018). A model of formative automatic assessment and interactive feedback for STEM. IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC).
• McCallin, A., & McCallin, M. (2009). Professional Perspective-Factors influencing team working and strategies to facilitate successful collborative teamwork. New Zealand Journal of Physiotherapy, 37(2), 61.
• Mitra, A. (2016). Fundamentals of quality control and improvement. John Wiley & Sons.
• Nonaka, I., & Konno, N. (1998). The concept of "ba": Building a foundation for knowledge creation. California Management Review, 40(3), 40-54.
• Nonaka, I., & Teece, D.J. (2001). Managing industrial knowledge: creation, transfer and utilization. Sage.
• Pedraja-Rejas, L., Rodriguez-Ponce, E., Rodriguez Mardones, P., Ganga Contreras, F., & Villegas Villegas, F. (2016). Determinants of the level of satisfaction of students in their schools: An exploratory study in chile. Interciencia, 41(6), 401-406.
• Pinson, M.H., Staelens, N., & Webster, A. (2013). The history of video quality model validation. Multimedia Signal Processing (MMSP), IEEE 15th International Workshop on.
• Purdy, C., Wang, X., He, L., & Riedl, M. (2018). Predicting generated story quality with quantitative measures. Fourteenth Artificial Intelligence and Interactive Digital Entertainment Conference.
• Robertson, R. (2016). Globalization, cultural dimensions. The Wiley Blackwell Encyclopedia of Race, Ethnicity, and Nationalism.
• Saleh, S.D., & Otis, J.L. (1964). Age and level of job satisfaction. Personnel Psychology, 17(4), 425-430.
• Schwartz, S.H. (1994). Beyond individualism/collectivism: New cultural dimensions of values. Sage Publications, Inc.
• Sergiovanni, T. (1967). Factors which affect satisfaction and dissatisfaction of teachers. Journal of Educational Administration, 5(1), 66-82.
• Sharma, T., Kechagia, M., Georgiou, S., Tiwari, R., Vats, I., Moazen, H., & Sarro, F. (2021). A survey on machine learning techniques for source code analysis. arXiv preprint arXiv:2110.09610.
• Shewfelt, R.L. (1999). What is quality? Postharvest Biology and Technology, 15(3), 197-200.
• Shi, G. (2013). Data mining and knowledge discovery for geoscientists. Elsevier.
• Silitonga, P. (2021). Competency-based education: A multi-variable study of tourism vocational high school graduates. Journal of Teaching in Travel & Tourism, 21(1), 72-90.
• Singh, M., Gupta, P.K., Tyagi, V., Sharma, A., Ören, T., & Grosky, W. (2017). Advances in computing and data sciences: First international conference, ICACDS 2016, Ghaziabad, India, November 11-12, Revised Selected Papers (Vol. 721). Springer.
• Thireau, M. (2002). Does brain weight have meaning. The bi-monthly Journal of The BWW Society, 2(2), 1-3.
• Tian, D., Jia, X., Ma, R., Liu, S., Liu, W., & Hu, C. (2021). BinDeep: A deep learning approach to binary code similarity detection. Expert Systems with Applications, 168, 114348.
• Vassiliadis, S., & Schwarz, E.M. (1990). High speed parity prediction for binary adders using irregular grouping scheme. In: Google Patents.
• Wang, A. (2023). Embedded system architecture-computer embedded software defect prediction based on genetic optimisation algorithms. International Journal of Information Technology and Management, 22(3-4), 262-280.
• Wittenberg, E., Kravits, K., Goldsmith, J., Ferrell, B., & Fujinami, R. (2016). Validation of a model of family caregiver communication types and related caregiver outcomes. Palliative & Supportive Care, 1-9.
• Wohlfart, O., Adam, S., & Hovemann, G. (2022). Aligning competence-oriented qualifications in sport management higher education with industry requirements: An importance–performance analysis. Industry and Higher Education, 36(2), 163-176.
• Woods, J. (2018). Against reflective equilibrium for logical theorizing.
• Zhang, Z. (2023). Revamping Binary Analysis with Sampling and Probabilistic Inference Purdue University Graduate School.