Abstract
Effective teachers are strongly committed to creating a positive learning experience, instinct, and impact for transforming students’ learning. The transforming elements can be defined through related attributes, such as self-efficacy, regular attendance, and cooperative behavior. However, this involves a significant data analysis task to measure teachers’ performance and predict their effectiveness in the education domain. Underpinned by a recognized design perspective of design science research, this study establishes a methodological framework for designing a solution artifact utilizing machine learning algorithms informing the design science research for IS design. We designed a new solution artifact utilizing a case dataset, specifically, a record of the UCI machine learning repository. Researchers can measure teachers’ effectiveness through the proposed innovative technique that elevates distinct resources to configure learning opportunities and relevant monitoring of learning. To evaluate the proposed ML model in measuring teachers’ effectiveness, we validated the prediction by contrasting it with other comparable models. We developed two ML models using K-means and hierarchical algorithms and found that the K-means presented the best outcome in representing three clusters: negative, positive, and neutral feedback, also showing 99% accuracy using the random forest classifier. Therefore, the K-means clustering technique is selected to be the core component of the solution for predicting teachers’ effectiveness.
Similar content being viewed by others
References
Abaidullah AM, Ahmed N, Ali E (2015) Identifying hidden patterns in students’ feedback through cluster analysis. Int J of Comput Theory Eng 7(1):16
Abdulhafedh A (2021) Incorporating K-means, hierarchical clustering and PCA in customer segmentation. J City Dev 3(1):12–30
Agaoglu M (2016) Predicting instructor performance using data mining techniques in higher education. IEEE Access 4:2379–2387
Ahmadi F, Ahmad MS (2013) Data mining in teacher evaluation system using WEKA. Int J Comput Appl 63(10):12–18
Ahmed AM, Rizaner A, Ulusoy AH (2016) Using data mining to predict instructor performance. Procedia Comput Sci 102:137–142
Anderson LW (2004) Increasing teacher effectiveness. International publication institute for education planning assessment of teacher effectiveness of human resources. UNESCO 41(4):778–820
Asanbe M, Osofisan A, William W (2016) Teachers’ performance evaluation in the higher educational institution using data mining technique. Int J Appl Inf Syst (IJAIS) 10:10–15
Baradwaj BK, Pal S (2012) Mining educational data to analyse students’ performance. arXiv:1201.3417
Bergmann P, Fauser M, Sattlegger D, Steger C (2020) Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In: Paper presented at the proceedings of the IEEE/CVF conference on computer vision and pattern recognition 4183–4192
Block E, Crochet F, Jones L, Papa T (2012) The importance of teacher’s effectiveness. Creat Educ 3(06):1164
Brna P (1999) Artificial intelligence in educational software: has its time come? Br J Educ Technol 30(1):79–81
Cheng X, Sun J, Zarifis A (2020) Artificial intelligence and deep learning in educational technology research and practice. Br J Educ Technol 51(5):1653–1656
Clark D (1993) Teacher evaluation: a review of the literature with implications for educators, Thesis paper, California State University at Long Beach, Retrieved from https://eric.ed.gov/?id=ED359174. Accessed 19 July 2023
Deb AB, Dey L (2017) Outlier detection and removal algorithm in K-means and hierarchical clustering. World J Comput Appl Technol 5(2):24–29
Doan S, Schweig JD, Mihaly K (2019) The consistency of composite ratings of teacher effectiveness: evidence from New Mexico. Am Educ Res J 56(6):2116–2146
Fahd K, Miah SJ, Ahmed K, Venkatraman S, Miao Y (2021) Integrating design science research and design based research frameworks for developing education support systems. Educ Inf Technol 26:4027–4048
Fahd K, Venkatraman S, Miah SJ, Ahmed K (2022) Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature. Educ Inf Technol 27(1):1–33
Goe L, Bell C, Little O (2008) Approaches to evaluating teacher effectiveness: a research synthesis. Evaluation report, National Comprehensive Center for Teacher Quality, p. 1-100. Retrieved from: https://files.eric.ed.gov/fulltext/ED521228.pdf. Accessed 24 July 2023
Goldhaber D, Anthony E (2007) Can teacher quality be effectively assessed? national board certification as a signal of effective teaching. Rev Econ Stat 89(1):134–150
Gregor S, Hevner AR (2013) Positioning and presenting design science research for maximum impact. MIS Q 37(2):337–355
Gupta A, Sharma H, Akhtar A (2021) A comparative analysis of K-means and hierarchical clustering EPRA. Int J Multidiscip Res (IJMR). https://doi.org/10.36713/epra8308
Heck RH (2009) Teacher effectiveness and student achievement: investigating a multilevel cross-classified model. J Educ Adm 47(2):227–249
Hemaid RK, El-Halees AM (2015) Improving teacher performance using data mining. Int J Adv Res Comput Commun Eng 4(2):407–412
Hershberg T, Simon VA, Lea-Kruger B (2004) The revelations of value-added: an assessment model that measures student growth in ways that NCLB fails to do. Sch Adm 61(11):10
Hevner AR (2007) A three cycle view of design science research. Scand J Inf Syst 19(2):4
Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28(1):75–105
Hevner A, Chatterjee S (2010) Design science research in information systems In: Shardr R, Vob S (eds) Design Research in Information Systems Theory and Practice. Springer, p 9–22. Retrieved from https://link.springer.com/chapter/10.1007/978-1-4419-5653-8_2. Accessed 24 July 2023
Irshadullah FKHM (2018) A review of the effect of education and good trained teachers on students’ performance. Humanit Soc Sci 25:93–99
Järvelä S, Gašević D, Seppänen T, Pechenizkiy M, Kirschner PA (2020) Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning. Br J Educ Technol 51(6):2391–2406
Järvinen P (2007) Action research is similar to design science. Qual Quant 41:37–54
Kane TJ, McCaffrey DF, Miller T, Staiger DO (2013) Have we identified effective teachers? validating measures of effective teaching using random assignment. In: Paper presented at the research paper. MET Project. Bill & Melinda Gates Foundation
Kumar S (2017) A modern data mining method for assessment of teaching assistant in higher educational institutions. Int J Comput Sci Inf Technol 8(3):424–429
Lupascu AR, Pânisoară G, Pânisoară I-O (2014) Characteristics of effective teacher. Procedia Soc Behav Sci 127:534–538
Mardikyan S, Badur B (2011) Analyzing teaching performance of instructors using data mining techniques. Inform Educ 10(2):245–257
Miah SJ (2009) End user as application developer for decision support. In: Proceedings of the American conference on information systems, (AMCIS 2009), Sun Francesco, USA, 142
Miah SJ, McGrath GM, Kerr D (2016) Design science research for decision support systems development: recent publication trends in the premier IS journals. Australas J Inf Syst 20:1–14
Mohan M, Hull RE (1975). Teaching effectiveness: Its meaning, assessment, and improvement. Englewood Cliffs, NJ: Educational Technology Publications. Retrieved from https://books.google.com.au/books?id=ouEyYjPAMxUC. Accessed 24 July 2023
Niemi H, Tirri K (1996) Effectiveness of teacher education. New challenges and approaches to evaluation. Reports from the Department of Teacher Education in Tampere University. ERIC
Ogor EN (2007) Student academic performance monitoring and evaluation using data mining techniques. In: Paper presented at the electronics, robotics and automotive mechanics conference (CERMA 2007)
Ola A, Pallaniappan S (2013) A data mining model for evaluation of instructors’ performance in higher institutions of learning using machine learning algorithms. Int J Concep Comput Inf Technol 1(1):17–22
Onafowora LL (2005) Teacher efficacy issues in the practice of novice teachers. Educ Res Q 28(4):34–43
Orozco W, Rodríguez-García MÁ, Fernández A (2019) Teaching effectiveness: an innovative evaluation model. In: International workshop on learning technology for education in cloud. Springer International Publishing, Cham, pp 450–461
Pal AK, Pal S (2013) Evaluation of teacher’s performance: a data mining approach. Int J Comput Sci Mob Comput 2(12):359–369
Papamitsiou ZK, Economides AA (2014) Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. J Educ Technol Soc 17(4):49–64
Parihar R (2011) Concept of Teacher Effectiveness. New Dehli: Jaypee Brother Publications Policy Implications. USA: University of Missouri-Columbia
Peffers K, Tuunanen T, Rothenberger MA, Chatterjee S (2007) A design science research methodology for information systems research. J Manag Inf Syst 24(3):45–77
Sanders WL (2000) Value-added assessment from student achievement data: opportunities and hurdles CREATE NATIONAL EVALUATION INSTITUTE July 21, 2000. J Pers Eval Educ 14(4):329–339
Shinkfield AJ, Stufflebeam DL (2012) Teacher evaluation: Guide to effective practice (Vol. 41). Springer, Berlin
Skourdoumbis A (2014) Teacher effectiveness: making the difference to student achievement? Br J Educ Stud 62(2):111–126
Tschannen-Moran M, Hoy AW (2001) Teacher efficacy: capturing an elusive construct. Teach Teach Educ 17(7):783–805
Vaishnavi VK (2007) Design science research methods and patterns: innovating information and communication technology. Auerbach Publications, New York
Vaishnavi VK, Kuechler W (2007) Introduction to design science research in information and communication technology. In: Vaishnavi VK, Vaishnavi VK, Kuechler W (eds) Design science research methods and patterns: innovating information and communication technology. Taylor & Francis Group, United Kingdom
Vijayalakshmi V, Panimalar K, Janarthanan S (2020) Predicting the performance of instructors using Machine-learning algorithms. High Technol Lett 26(12):694–705
Williams K, Hebert D (2020) Teacher evaluation systems: a literature review on issues and impact. Res Issues Contemp Educ 5(1):42–50
Yadav SK, Bharadwaj B, Pal S (2012). Mining education data to predict student’s retention: a comparative study. arXiv:1203.2987
Zakaria I, Nor MYBM, Alias B (2021) The effect of teachers’ professionalism on students’ success. Int J Acad Res Bus Soc Sci 11(1):483–500
Zawacki-Richter O, Marín VI, Bond M, Gouverneur F (2019) Systematic review of research on artificial intelligence applications in higher education—where are the educators. Int J Educ Technol High Educ. https://doi.org/10.1186/s41239-019-0171-0
Zhou H, Lu J, Huang Y, Chen Y (2021) Research on key technology of classroom teaching evaluation based on artificial intelligence. In Journal of Physics: Conference series (Vol. 1757, No. 1, p. 012014). IOP publishing
Author information
Authors and Affiliations
Contributions
Both authors contribute equally to develop the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix 1: Questions
Appendix 1: Questions
-
instr: instructor’s identifier; values taken from {1, 2, 3}
-
class: course code (descriptor); values taken from {1–13}
-
repeat: number of times the student is taking this course; values are taken from {0, 1, 2, 3, …}
-
attendance: code of the level of attendance; values from {0, 1, 2, 3, 4}
-
difficulty: level of difficulty of the course as perceived by the student; values taken from {1, 2, 3, 4, 5}
-
Q1: the semester course content, teaching method and evaluation system were provided initially.
-
Q2: the course aims and objectives were clearly stated at the beginning of the period.
-
Q3: the course was worth the number of credits assigned to it.
-
Q4: the course was taught according to the syllabus announced on the first day of class.
-
Q5: the class discussions, homework assignments, applications, and studies were satisfactory.
-
Q6: the textbook and other course resources were sufficient and up to date.
-
Q7: the course allowed for fieldwork, applications, laboratory work, discussions, and other studies.
-
Q8: the quizzes, assignments, projects, and exams contributed to the learning.
-
Q9: i greatly enjoyed the class and actively participated during the lectures.
-
Q10: my initial expectations about the course were met at the end of the period or year.
-
Q11: the course was relevant and beneficial to my professional development.
-
Q12: the course helped me look at life and the world from a new perspective.
-
Q13: the instructor’s knowledge was relevant and up to date.
-
Q14: the instructor came prepared for classes.
-
Q15: the instructor taught in accordance with the announced lesson plan.
-
Q16: the instructor was committed to the course and was understandable.
-
Q17: the instructor arrived on time for classes.
-
Q18: the instructor has a smooth and easy-to-follow delivery/speech.
-
Q19: the instructor made effective use of class hours.
-
Q20: the instructor explained the course and was eager to help students.
-
Q21: the instructor demonstrated a positive approach to students.
-
Q22: the instructor was open and respectful of students’ views about the course.
-
Q23: the instructor encouraged participation in the course.
-
Q24: the instructor gave relevant homework assignments/projects and helped/guided students.
-
Q25: the instructor responded to questions about the course inside and outside of the course.
-
Q26: the instructor’s evaluation system (midterm and final questions, projects, assignments) effectively measured the course objectives.
-
Q27: the instructor provided solutions to exams and discussed them with students.
-
Q28: the instructor treated all students in a right and objective manner.
Note: Q1–Q28 are all Likert-type, meaning that the values are taken from {1, 2, 3, 4, 5}.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sabharwal, R., Miah, S.J. Evaluating teachers’ effectiveness in classrooms: an ML-based assessment portfolio. Soc. Netw. Anal. Min. 14, 28 (2024). https://doi.org/10.1007/s13278-023-01195-5
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13278-023-01195-5