Planning, Conducting and Reporting the Review of Employability using Data Mining and Predictive Analysis

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    A new research involves the collection and analysis of several research papers and it requires systematic methods of identifying the gaps and generating reliable evidence as a whole. Research questions can be drawn through the results of a systematic study that summarizes the overall research thoroughly. This paper aims giving an exposure on the systematic review used in the study of graduates' employability using data mining techniques and predictive analysis. Three main processes; (i) planning, (ii) conducting and (iii) reporting the review have been conducted to answer the research questions on predictive analysis conducted on the problem of employability among the fresh graduates. The methods of the review and specifies the research questions described through a review protocol involving three main databases; (i) Scopus, (ii) ScienceDirect and (iii) Web of Science. A total of 120 journal articles are classified into three main categories through taxonomy analysis. The results of the study are discussed in three main aspects; (i) challenges, (ii) motivations and (iii) recommendations while the research interest of the analysis results is critically formulated under critical review.


  • Keywords

    Data Mining; Predictive Analysis; Review Protocol; Systematic Review; Taxonomy Analysis.

  • References

      [1] Achmad, H., Sabur, V. F., Pritasari, A., & Reinaldo, H. (2016). Sains Humanika Data Mining and Sharing to Create Usable Knowledge, Implementation in Small Business in Indonesia, 2(2016), 69–75.

      [2] Romero, C., & Ventura, S. (2013). Data mining in education, 3(February), 12–27.

      [3] Yusof, Y., & Refai, M. H. (2013). Modified Multi-Class Classification using Association Rule Mining, 21(1), 205–216. Retrieved from PAPERS/JST Vol. 21 (1) Jan. 2013/19. Page 205-216. pdf%5Cnpapers2://publication/uuid/6928FCF9-07B0-45A4-B511-85643042723B

      [4] Lakshmi Priya, G., & Hariharan, S. (2012). A study on predicting patterns over the protein sequence datasets using association rule mining. Journal of Engineering Science and Technology, 7(5), 563–573.

      [5] Kitchenham, B. (2004). Procedures for Performing Systematic Reviews.

      [6] Benbow, C. P. (2012). Identifying and Nurturing Future Innovators in Science , Technology , Engineering , and Mathematics : A Review of Findings From the Study of Mathematically Precocious Youth Identifying and Nurturing Future Innovators in Science , Technology , Engineering , and Mathematics : A Review of Findings From the Study of Mathematically Precocious Youth, (October 2014), 37–41.

      [7] Cantwell, B., & Taylor, B. J. (2013). Internationalization of the postdoctorate in the United States : analyzing the demand for international postdoc labor, 551–567.

      [8] Rhodes, R. E., & Quinlan, A. (2015). Predictors of Physical Activity Change Among Adults Using Observational Designs, 423–441.

      [9] Stone, A. L., Becker, L. G., Huber, A. M., & Catalano, R. F. (2012). Addictive Behaviors Review of risk and protective factors of substance use and problem use in emerging adulthood. Addictive Behaviors, 37(7), 747–775.

      [10] Arsad, P. M., Buniyamin, N., & Manan, J. A. (2014). Neural Network and Linear Regression Methods for Prediction of Students ’ Academic Achievement, (April), 916–921.

      [11] Beck, H. P., & Milligan, M. (2014). Internet and Higher Education Factors in fl uencing the institutional commitment of online students. The Internet and Higher Education, 20, 51–56.

      [12] Deepak, E., Pooja, G. S., Jyothi, R. N. S., Kumar, S. V. P., & Kishore, K. V. (n.d.). SVM Kernel based Predictive Analytics on Faculty Performance Evaluation.

      [13] Hsia, J. (2015). mobile learning adoption. Journal of Computing in Higher Education, (707).

      [14] Li, Z. (2016). A Novel Multidimensional Professionalism Evaluation Model.

      [15] Rasipuram, S., B, P. R. S., & Jayagopi, D. B. (n.d.). Automatic Prediction of Fluency in Interface-based Interviews.

      [16] Ghosh, A., & Fouad, N. A. (2016). Career Transitions of Student Veterans, 24(1), 99–111.

      [17] Buchanan, K., & Bardi, A. (2015). The Roles of Values , Behavior , and Value-Behavior Fit in the Relation of Agency and Communion to Well-Being, (June).

      [18] Freyr, H., Halldorsson, F., & Kristinsson, K. (2016). Personality in Gneezy â€TM s cheap talk game : The interaction between Honesty-Humility and Extraversion in predicting deceptive behavior. PAID, 96, 222–226.

      [19] Liaw, S., & Huang, H. (2013). Computers & Education Perceived satisfaction , perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14–24.

      [20] Counseling, P., & Program, G. (2013). MINDFULNESS AND FIVE FACTOR PERSONALITY TRAITS, 33–45.

      [21] Scrimin, S., & Mason, L. (2015). Does mood influence text processing and comprehension ? Evidence from an eye-movement study, 387–406.

      [22] Shokri, O., & Potenza, M. N. (2017). O R I G I N A L A RT I C L E Between Impulsivity and Severity of Problematic Internet Use in Male and Female Iranian College Students.

      [23] Tang, C., & Ding, X. (2014). Computers in Human Behavior Graduate students ’ creative professional virtual community behaviors and their creativity. COMPUTERS IN HUMAN BEHAVIOR.

      [24] Zamani-miandashti, N., Memarbashi, P., Khalighzadeh, P., Zamani-miandashti, N., & Memarbashi, P. (2014). The International Information & Library Review The prediction of Internet utilization behavior of undergraduate agricultural students : An application of the theory of planned behavior The prediction of Internet utilization behavior of undergraduate agricultural students : An application of the theory of planned behavior, (January 2015), 37–41.

      [25] Zanardelli, G., Shivy, V. A., & Perrone-mcgovern, K. M. (2016). separation relationships, 53(December), 162–173.

      [26] Ibrahim, I. I., Noor, S. M., Nasirun, N., & Ahmad, Z. (2012). Safety in The Office : Does It Matter to The Staff ? Procedia - Social and Behavioral Sciences, 50(July), 730–740.

      [27] Authors, F. (2017). The International Journal of Information and Learning Technology Article information:

      [28] Cucina, J. M., Su, C., Busciglio, H. H., & Peyton, S. T. (2015). Intelligence Something more than g : Meaningful Memory uniquely predicts training performance ☆. Intelligence, 49, 192–206.

      [29] Taylor, T. Z., Heijden, B. I. J. M. V. A. N. D. E. R., & Genuchi, M. C. (2017). The Police Of fi cer Tacit Knowledge Inventory ( POTKI ): Towards Determining Underlying Structure and Applicability as a Recruit Screening Tool, 246, 236–246.

      [30] Zacher, H. (2016). Within-person relationships between daily individual and job characteristics and daily manifestations of career adaptability. Journal of Vocational Behavior, 92, 105–115.

      [31] Liu, W., & Cross, J. A. (2016). ScienceDirect A comprehensive model of project team technical performance. JPMA, 34(7).

      [32] Fazilat-pour, M. A. M. (2015). Simple and Multivariate Relationships Between Spiritual Intelligence with General Health and Happiness.

      [33] Yang, J., Development, E., Human, S., Management, R., & Development, E. (2013). THE THEORY OF PLANNED BEHAVIOR AND PREDICTION OF ENTREPRENEURIAL INTENTION AMONG CHINESE UNDERGRADUATES, 41(71002112), 367–376.

      [34] International, D. D. (2015). Specificity Matters : Criterion-Related Validity of Contextualized and Facet Measures of Conscientiousness in Predicting College Student Performance, 97(3), 301–309.

      [35] Stoll, G., Rieger, S., Lüdtke, O., Nagengast, B., Trautwein, U., Brent, W., … Roberts, B. W. (2016). Journal of Personality and Social Psychology Vocational Interests Assessed at the End of High School Predict Life Outcomes Assessed 10 Years Later Over and Above IQ and Big Five Personality Traits Vocational Interests Assessed at the End of High School Predict Life Outcomes Assessed 10 Years Later Over and Above IQ and Big Five.

      [36] Sheldon, K. M., Turban, D. B., Brown, K. G., Barrick, M. R., & Judge, T. A. (2012). Motivation to learn and learning strategies IT courses in a library and information.

      [37] Kerssen-griep, J., & Witt, P. L. (2015). Instructional Feedback III : How Do Instructor Facework Tactics and Immediacy Cues Interact to Predict Student Perceptions of Being Mentored ?, (May), 37–41.

      [38] Adam, J., Bore, M., Mckendree, J., Munro, D., & Powis, D. (2012). Can personal qualities of medical students predict in-course examination success and professional behaviour ? An exploratory prospective cohort study, 1–8.

      [39] Chen, C. C. C. H. I. (2013). The relationship between the playfulness climate in the classroom and student creativity, 1493–1510.

      [40] Cooke, R., Dahdah, M., Norman, P., French, D. P., Cooke, R., Dahdah, M., … How, D. P. F. (2014). How well does the theory of planned behaviour predict alcohol consumption ? A systematic review and meta-analysis, 7199(November 2015).

      [41] Aharony, N. (2013). Librarians ’ attitudes towards mobile services, (2011).

      [42] Taylor, P., Ye, T., & Pan, X. (2015). A convenient prediction model for complete recovery time after exhaustion in high-intensity work, (March), 37–41.

      [43] Chen, I. (2016). Computers in Human Behavior Work engagement and its antecedents and consequences : A case of lecturers teaching synchronous distance education courses. Computers in Human Behavior, 1–9.

      [44] Garn, A., & Shen, B. (n.d.). International Journal of Sport and Physical self-concept and basic psychological needs in exercise : Are there reciprocal effects ?, (May 2015), 37–41.

      [45] Thompson, M. N., Nitzarim, R. S., & Her, P. (2015). Financial Stress and Work Hope Beliefs Among Adolescents, 1–14.

      [46] Tsao, J., & Wang, C. (2017). The Effects of Writing Anxiety and Motivation on EFL College Students ’ Self-Evaluative Judgments of Corrective Feedback.

      [47] Marks, A. B., & Moss, S. A. (2016). What Predicts Law Student Success ? A Longitudinal Study Correlating Law Student Applicant Data and Law School Outcomes, 13(2), 205–265.

      [48] Viola, M., Feldt, R., & Angelis, L. (2014). Personality , emotional intelligence and work preferences in software engineering : An empirical study. INFORMATION AND SOFTWARE TECHNOLOGY.

      [49] Bertholet, N., Gaume, J., Faouzi, M., Gmel, G., & Daeppen, J. (2012). Predictive value of readiness , importance , and confidence in ability to change drinking and smoking.

      [50] Pienaar, J., & Zhao, X. (2017). Factors Influencing Student Progression in Built Environment and Engineering Programs : Case of Central Queensland University, 143(4), 1–9.

      [51] Fischer, F. T., Schult, J., & Hell, B. (2013). Sex-Specific Differential Prediction of College Admission Tests : A Meta-Analysis, 105(2), 478–489.

      [52] Matherly, L. L. (2012). A causal model predicting student intention to enrol moderated by university image : using strategic management to create competitive advantage in higher education, 6, 38–55.

      [53] Schmitt, N. (2012). Development of Rationale and Measures of Noncognitive College Student Potential, 47(1), 18–29.

      [54] Duckworth, A. L., Quinn, P. D., & Tsukayama, E. (2012). What No Child Left Behind Leaves Behind : The Roles of IQ and Self-Control in Predicting Standardized Achievement Test Scores and Report Card Grades, 104(2), 439–451.

      [55] Educ, S. P. (2014). Teachers ’ high maintenance behaviour as perceived by university students in Taiwan , and their coping strategies, (32).

      [56] Abdullah, F., Ward, R., & Ahmed, E. (2016). Computers in Human Behavior Investigating the in fl uence of the most commonly used external variables of TAM on students ’ Perceived Ease of Use ( PEOU ) and Perceived Usefulness ( PU ) of e-portfolios. Computers in Human Behavior, 63, 75–90.

      [57] Masserini, L., Bini, M., & Pratesi, M. (2016). for predicting first-year performance in university career : a zero-inflated beta regression approach. Quality & Quantity.

      [58] Fischbach, A., Keller, U., Preckel, F., & Brunner, M. (2013). PISA pro fi ciency scores predict educational outcomes. Learning and Individual Differences, 24, 63–72.

      [59] Ain, N., Kaur, K., & Waheed, M. (2015). The influence of learning value on learning management system use : An extension of UTAUT2.

      [60] Frisby, B. N., Slone, A. R., & Bengu, E. (2016). Rapport , motivation , participation , and perceptions of learning in U . S . and Turkish student classrooms : a replication and cultural comparison. Communication Education, 0(0), 1–13.

      [61] Creed, P. A., & Hughes, T. (2013). Journal of.

      [62] Hottenrott, H., & Lawson, C. (2015). Studies in Higher Education Flying the nest : how the home department shapes researchers ’ career paths, 5079(October).

      [63] Huang, J. (2014). Hardiness , Perceived Employability , and Career Decision Self-Efficacy Among Taiwanese College Students, (415), 1–14.

      [64] Prouty, A. M., Helmeke, K. B., & Fischer, J. (2015). Development of the “‘ Mentorship in Clinical Training Scale ’” ( MiCTS ). Contemporary Family Therapy.

      [65] Aharony, N. (2014). Journal of Librarianship and Information Science.

      [66] Beccaria, L., Kek, M., Huijser, H., Rose, J., & Kimmins, L. (2014). Nurse Education Today The interrelationships between student approaches to learning and group work. YNEDT.

      [67] Bekiari, A. (2012). Perceptions of instructor’s verbal aggressiveness and physical education students’ affective learning 1, 2, 325–335.

      [68] Bozeman, B., Fay, D., & Gaughan, M. (2013). Power to Do … What ? Department Heads ’ Decision Autonomy and Strategic Priorities, 303–328.

      [69] Chin, E. C. H., Williams, M. W., Taylor, J. E., & Harvey, S. T. (2017). The influence of negative affect on test anxiety and academic performance : An examination of the tripartite model of emotions. Learning and Individual Differences, 54, 1–8.

      [70] The, U., Model, C. F., Compute, T. O., Probability, T. H. E., Detecting, O. F., Bias, P., … Court, S. (2012). Using the criterion-predictor factor model to compute the probability of detecting prediction bias with ordinary least squares regression, 561–580.

      [71] Hamaideh, S. H., & Hamdan-mansour, A. M. (2014). Nurse Education Today Psychological , cognitive , and personal variables that predict college academic achievement among health sciences students ☆. YNEDT, 34(5), 703–708.

      [72] Kim, C., Park, S. W., & Cozart, J. (2014). mathematics courses, 45(1), 171–185.

      [73] Schultz, N. M., Wong, W. B., Coleman, A. L., & Malone, D. C. (2016). AC. American Journal of Ophthalmology.

      [74] Bickerton, G. R., Miner, M. H., Dowson, M., & Griffin, B. (2015). Incremental Validity of Spiritual Resources in the Job Demands-Resources Model, 7(2), 162–172.

      [75] Chuang, S., Lin, F., & Tsai, C. (2015). Computers in Human Behavior An exploration of the relationship between Internet self-efficacy and sources of Internet self-efficacy among Taiwanese university students. COMPUTERS IN HUMAN BEHAVIOR, 48, 147–155.

      [76] Iglesias-pradas, S., Ruiz-de-azcárate, C., & Agudo-peregrina, Á. F. (2015). Computers in Human Behavior Assessing the suitability of student interactions from Moodle data logs as predictors of cross-curricular competencies. Computers in Human Behavior, 47, 81–89.


      [78] Buckless, F., & Krawczyk, K. (2016). The relation of student engagement and other admission metrics to Master of Accounting student performance, 9284(September).

      [79] Heller, M. L., & Cassady, J. C. (2015). Predicting Community College and University Student Success : A Test of the Triadic Reciprocal Model for Two Populations.

      [80] The Relationship between Perceived Organizational Support and Organizational Cynicism of Research. (2014), 14(1), 125–134.

      [81] C.K. Lim, Tan, K. L., Yusran, H., Suppramaniam, V., Kim, C., Tan, K. L Suppramaniam, V. (2017). score generation using visual language programming Comparison of L-System Applications Towards Plant Modelling , Music Rendering and Score Generation Using Visual Language Programming, 20086




Article ID: 22048
DOI: 10.14419/ijet.v7i4.19.22048

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.