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

  • Authors

    • KianLam Tan
    • Nor Azziaty Abdul Rahman
    • ChenKim Lim
    https://doi.org/10.14419/ijet.v7i4.19.22048

    Received date: November 28, 2018

    Accepted date: November 28, 2018

    Published date: November 27, 2018

  • Data Mining, Predictive Analysis, Review Protocol, Systematic Review, Taxonomy Analysis.

  • 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.

  • 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. https://doi.org/10.1002/widm.1075
    3. Yusof, Y., & Refai, M. H. (2013). Modified Multi-Class Classifica-tion using Association Rule Mining, 21(1), 205–216. Retrieved from http://www.pertanika2.upm.edu.my/Pertanika 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 Re-views.
    6. Benbow, C. P. (2012). Identifying and Nurturing Future Innovators in Science , Technology , Engineering , and Mathematics : A Re-view of Findings From the Study of Mathematically Precocious Youth Identifying and Nurturing Future Innovators in Science , Technology , Engineering , and Mathematics : A Review of Find-ings From the Study of Mathematically Precocious Youth, (October 2014), 37–41. https://doi.org/10.1080/0161956X.2012.642236
    7. Cantwell, B., & Taylor, B. J. (2013). Internationalization of the postdoctorate in the United States : analyzing the demand for inter-national postdoc labor, 551–567. https://doi.org/10.1007/s10734-013-9621-0
    8. Rhodes, R. E., & Quinlan, A. (2015). Predictors of Physical Activi-ty Change Among Adults Using Observational Designs, 423–441. https://doi.org/10.1007/s40279-014-0275-6
    9. Stone, A. L., Becker, L. G., Huber, A. M., & Catalano, R. F. (2012). Addictive Behaviors Review of risk and protective factors of sub-stance use and problem use in emerging adulthood. Addictive Be-haviors, 37(7), 747–775. https://doi.org/10.1016/j.addbeh.2012.02.014
    10. Arsad, P. M., Buniyamin, N., & Manan, J. A. (2014). Neural Net-work 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 stu-dents. The Internet and Higher Education, 20, 51–56. https://doi.org/10.1016/j.iheduc.2013.09.002
    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). https://doi.org/10.1007/s12528-015-9103-8
    14. Li, Z. (2016). A Novel Multidimensional Professionalism Evalua-tion Model.
    15. Rasipuram, S., B, P. R. S., & Jayagopi, D. B. (n.d.). Automatic Pre-diction of Fluency in Interface-based Interviews.
    16. Ghosh, A., & Fouad, N. A. (2016). Career Transitions of Student Veterans, 24(1), 99–111. https://doi.org/10.1177/1069072714568752
    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). https://doi.org/10.1111/jopy.12106
    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. https://doi.org/10.1016/j.paid.2016.02.075
    19. Liaw, S., & Huang, H. (2013). Computers & Education Perceived satisfaction , perceived usefulness and interactive learning environ-ments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14–24. https://doi.org/10.1016/j.compedu.2012.07.015
    20. Counseling, P., & Program, G. (2013). MINDFULNESS AND FIVE FACTOR PERSONALITY TRAITS, 33–45. https://doi.org/10.21909/sp.2013.01.619
    21. Scrimin, S., & Mason, L. (2015). Does mood influence text pro-cessing and comprehension ? Evidence from an eye-movement study, 387–406. https://doi.org/10.1111/bjep.12080
    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. https://doi.org/10.1007/s11469-017-9738-y
    23. Tang, C., & Ding, X. (2014). Computers in Human Behavior Grad-uate students ’ creative professional virtual community behaviors and their creativity. COMPUTERS IN HUMAN BEHAVIOR. https://doi.org/10.1016/j.chb.2014.09.055
    24. Zamani-miandashti, N., Memarbashi, P., Khalighzadeh, P., Zamani-miandashti, N., & Memarbashi, P. (2014). The International Infor-mation & Library Review The prediction of Internet utilization be-havior 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. https://doi.org/10.1080/10572317.2013.10766379
    25. Zanardelli, G., Shivy, V. A., & Perrone-mcgovern, K. M. (2016). separation relationships, 53(December), 162–173. https://doi.org/10.1002/joec.12041
    26. Ibrahim, I. I., Noor, S. M., Nasirun, N., & Ahmad, Z. (2012). Safe-ty in The Office : Does It Matter to The Staff ? Procedia - Social and Behavioral Sciences, 50(July), 730–740. https://doi.org/10.1016/j.sbspro.2012.08.076
    27. Authors, F. (2017). The International Journal of Information and Learning Technology Article information: https://doi.org/10.1108/IJILT-11-2016-0051
    28. Cucina, J. M., Su, C., Busciglio, H. H., & Peyton, S. T. (2015). In-telligence Something more than g : Meaningful Memory uniquely predicts training performance ☆. Intelligence, 49, 192–206. https://doi.org/10.1016/j.intell.2015.01.007
    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 ( POT-KI ): Towards Determining Underlying Structure and Applicability as a Recruit Screening Tool, 246, 236–246. https://doi.org/10.1002/acp.3321
    30. Zacher, H. (2016). Within-person relationships between daily indi-vidual and job characteristics and daily manifestations of career adaptability. Journal of Vocational Behavior, 92, 105–115. https://doi.org/10.1016/j.jvb.2015.11.013
    31. Liu, W., & Cross, J. A. (2016). ScienceDirect A comprehensive model of project team technical performance. JPMA, 34(7). https://doi.org/10.1016/j.ijproman.2016.05.011
    32. Fazilat-pour, M. A. M. (2015). Simple and Multivariate Relation-ships Between Spiritual Intelligence with General Health and Hap-piness. https://doi.org/10.1007/s10943-015-0004-y
    33. Yang, J., Development, E., Human, S., Management, R., & Devel-opment, 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 Conscientious-ness in Predicting College Student Performance, 97(3), 301–309. https://doi.org/10.1080/00223891.2014.1002134
    35. Stoll, G., Rieger, S., Lüdtke, O., Nagengast, B., Trautwein, U., Brent, W., … Roberts, B. W. (2016). Journal of Personality and So-cial 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 As-sessed 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. https://doi.org/10.1108/00242531211207415
    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. https://doi.org/10.1080/03634523.2014.978797
    38. Adam, J., Bore, M., Mckendree, J., Munro, D., & Powis, D. (2012). Can personal qualities of medical students predict in-course exami-nation success and professional behaviour ? An exploratory prospec-tive cohort study, 1–8.
    39. Chen, C. C. C. H. I. (2013). The relationship between the playful-ness climate in the classroom and student creativity, 1493–1510. https://doi.org/10.1007/s11135-011-9603-1
    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 re-view and meta-analysis, 7199(November 2015). https://doi.org/10.1080/17437199.2014.947547
    41. Aharony, N. (2013). Librarians ’ attitudes towards mobile services, (2011). https://doi.org/10.1108/AP-07-2012-0059
    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. https://doi.org/10.1080/00140139.2015.1008587
    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 Be-havior, 1–9. https://doi.org/10.1016/j.chb.2016.10.002
    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. https://doi.org/10.1080/1612197X.2014.940994
    45. Thompson, M. N., Nitzarim, R. S., & Her, P. (2015). Financial Stress and Work Hope Beliefs Among Adolescents, 1–14. https://doi.org/10.1177/1069072715621517
    46. Tsao, J., & Wang, C. (2017). The Effects of Writing Anxiety and Motivation on EFL College Students ’ Self-Evaluative Judgments of Corrective Feedback. https://doi.org/10.1177/0033294116687123
    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 em-pirical study. INFORMATION AND SOFTWARE TECHNOLO-GY. https://doi.org/10.1016/j.infsof.2014.03.004
    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 Pro-gression in Built Environment and Engineering Programs : Case of Central Queensland University, 143(4), 1–9. https://doi.org/10.1061/(ASCE)EI.1943-5541.0000341.
    51. Fischer, F. T., Schult, J., & Hell, B. (2013). Sex-Specific Differen-tial 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 manage-ment 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. https://doi.org/10.1080/00461520.2011.610680
    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. https://doi.org/10.1037/a0026280
    55. Educ, S. P. (2014). Teachers ’ high maintenance behaviour as per-ceived by university students in Taiwan , and their coping strategies, (32). https://doi.org/10.1007/s11218-013-9230-x
    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. Comput-ers in Human Behavior, 63, 75–90. https://doi.org/10.1016/j.chb.2016.05.014
    57. Masserini, L., Bini, M., & Pratesi, M. (2016). for predicting first-year performance in university career : a zero-inflated beta regres-sion approach. Quality & Quantity. https://doi.org/10.1007/s11135-016-0433-z
    58. Fischbach, A., Keller, U., Preckel, F., & Brunner, M. (2013). PISA pro fi ciency scores predict educational outcomes. Learning and In-dividual Differences, 24, 63–72. https://doi.org/10.1016/j.lindif.2012.10.012
    59. Ain, N., Kaur, K., & Waheed, M. (2015). The influence of learning value on learning management system use : An extension of UTAUT2. https://doi.org/10.1177/0266666915597546
    60. Frisby, B. N., Slone, A. R., & Bengu, E. (2016). Rapport , motiva-tion , participation , and perceptions of learning in U . S . and Turk-ish student classrooms : a replication and cultural comparison. Communication Education, 0(0), 1–13. https://doi.org/10.1080/03634523.2016.1208259
    61. Creed, P. A., & Hughes, T. (2013). Journal of. https://doi.org/10.1177/0894845312437207
    62. Hottenrott, H., & Lawson, C. (2015). Studies in Higher Education Flying the nest : how the home department shapes researchers ’ ca-reer paths, 5079(October). https://doi.org/10.1080/03075079.2015.1076782
    63. Huang, J. (2014). Hardiness , Perceived Employability , and Career Decision Self-Efficacy Among Taiwanese College Students, (415), 1–14. https://doi.org/10.1177/0894845314562960
    64. Prouty, A. M., Helmeke, K. B., & Fischer, J. (2015). Development of the “‘ Mentorship in Clinical Training Scale ’” ( MiCTS ). Con-temporary Family Therapy. https://doi.org/10.1007/s10591-015-9351-9
    65. Aharony, N. (2014). Journal of Librarianship and Information Sci-ence. https://doi.org/10.1177/0961000614532120
    66. Beccaria, L., Kek, M., Huijser, H., Rose, J., & Kimmins, L. (2014). Nurse Education Today The interrelationships between student ap-proaches to learning and group work. YNEDT. https://doi.org/10.1016/j.nedt.2014.02.006
    67. Bekiari, A. (2012). Perceptions of instructor’s verbal aggressiveness and physical education students’ affective learning 1, 2, 325–335. https://doi.org/10.2466/06.11.16.PMS.115.4.325-335
    68. Bozeman, B., Fay, D., & Gaughan, M. (2013). Power to Do … What ? Department Heads ’ Decision Autonomy and Strategic Pri-orities, 303–328. https://doi.org/10.1007/s11162-012-9270-7
    69. Chin, E. C. H., Williams, M. W., Taylor, J. E., & Harvey, S. T. (2017). The influence of negative affect on test anxiety and aca-demic performance : An examination of the tripartite model of emo-tions. Learning and Individual Differences, 54, 1–8. https://doi.org/10.1016/j.lindif.2017.01.002
    70. The, U., Model, C. F., Compute, T. O., Probability, T. H. E., De-tecting, O. F., Bias, P., … Court, S. (2012). Using the criterion-predictor factor model to compute the probability of detecting pre-diction bias with ordinary least squares regression, 561–580.
    71. Hamaideh, S. H., & Hamdan-mansour, A. M. (2014). Nurse Educa-tion Today Psychological , cognitive , and personal variables that predict college academic achievement among health sciences stu-dents ☆. YNEDT, 34(5), 703–708. https://doi.org/10.1016/j.nedt.2013.09.010
    72. Kim, C., Park, S. W., & Cozart, J. (2014). mathematics courses, 45(1), 171–185. https://doi.org/10.1111/j.1467-8535.2012.01382.x
    73. Schultz, N. M., Wong, W. B., Coleman, A. L., & Malone, D. C. (2016). AC. American Journal of Ophthalmology. https://doi.org/10.1016/j.ajo.2016.05.001
    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 Behav-ior 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. https://doi.org/10.1016/j.chb.2015.01.044
    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. https://doi.org/10.1016/j.chb.2014.09.065
    77. Bosompem, M., Dadzie, S. K. N., Tandoh, E., & Tandoh, E. (2017). UNDERGRADUATE STUDENTS ’ WILLINGNESS TO START OWN AGRIBUSINESS VENTURE AFTER GRADUATION : A GHANAIAN. https://doi.org/10.1108/S2040-724620170000007009
    78. Buckless, F., & Krawczyk, K. (2016). The relation of student en-gagement and other admission metrics to Master of Accounting student performance, 9284(September). https://doi.org/10.1080/09639284.2016.1218778
    79. Heller, M. L., & Cassady, J. C. (2015). Predicting Community Col-lege and University Student Success : A Test of the Triadic Recip-rocal Model for Two Populations. https://doi.org/10.1177/1521025115611130
    80. The Relationship between Perceived Organizational Support and Organizational Cynicism of Research. (2014), 14(1), 125–134. https://doi.org/10.12738/estp.2014.1.1765
    81. C.K. Lim, Tan, K. L., Yusran, H., Suppramaniam, V., Kim, C., Tan, K. L Suppramaniam, V. (2017). score generation using visual lan-guage programming Comparison of L-System Applications Towards Plant Modelling , Music Rendering and Score Generation Using Visual Language Programming, 20086

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    Tan, K., Azziaty Abdul Rahman, N., & Lim, C. (2018). Planning, Conducting and Reporting the Review of Employability using Data Mining and Predictive Analysis. International Journal of Engineering and Technology, 7(4.19), 199-206. https://doi.org/10.14419/ijet.v7i4.19.22048