Course Satisfaction in Engineering Education Through the Lens of Student Agency Analytics

This Research Full Paper presents an examination of the relationships between course satisfaction and student agency resources in engineering education. Satisfaction experienced in learning is known to benefit the students in many ways. However, the varying significance of the different factors of course satisfaction is not entirely clear. We used a validated questionnaire instrument, exploratory statistics, and supervised machine learning to examine how the different factors of student agency affect course satisfaction among engineering students (N = 293). Teacher’s support and trust for the teacher were identified as both important and critical factors concerning experienced course satisfaction. Participatory resources of agency and gender proved to be less important factors. The results provide convincing evidence about the possibility to identify the most important factors affecting course satisfaction.

[1]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[2]  Tommi Kärkkäinen,et al.  Feature Ranking of Large, Robust, and Weighted Clustering Result , 2017, PAKDD.

[3]  Clayton N. Tatro Gender effects on student evaluations of faculty , 1995 .

[4]  Christophe Ambroise,et al.  Feature selection in robust clustering based on Laplace mixture , 2006, Pattern Recognit. Lett..

[5]  S. Christenson,et al.  Handbook of Research on Student Engagement , 2012 .

[6]  Nur Riza Mohd Suradi,et al.  Modeling of Engineering Student Satisfaction , 2012 .

[7]  Lisa Linnenbrink-Garcia,et al.  Academic Emotions and Student Engagement , 2012 .

[8]  Tommi Kärkkäinen,et al.  Understanding the Study Experiences of Students in Low Agency Profile: Towards a Smart Education Approach , 2020, Advances in Smart Technologies Applications and Case Studies.

[9]  Mirka Saarela,et al.  Predicting hospital associated disability from imbalanced data using supervised learning , 2019, Artif. Intell. Medicine.

[10]  Andy Laws,et al.  Machine learning approaches to predict learning outcomes in Massive open online courses , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[11]  Gijsbert Erkens,et al.  Shared Epistemic Agency: An Empirical Study of an Emergent Construct , 2010 .

[12]  F. Reichheld The one number you need to grow. , 2003, Harvard business review.

[13]  Sioux McKenna,et al.  Possible futures for science and engineering education , 2016 .

[14]  Harold D. Delaney,et al.  The Kruskal-Wallis Test and Stochastic Homogeneity , 1998 .

[15]  Yair Levy,et al.  Comparing dropouts and persistence in e-learning courses , 2007, Comput. Educ..

[16]  George Siemens,et al.  Message from the LAK 2011 General & Program Chairs , 2011 .

[17]  Mirka Saarela,et al.  Can we automate expert-based journal rankings? Analysis of the Finnish publication indicator , 2020, J. Informetrics.

[18]  Kati Vasalampi,et al.  Assessing agency of university students: validation of the AUS Scale , 2017 .

[19]  Markku Antero Laitinen Net Promoter Score as Indicator of Library Customers' Perception , 2018 .

[20]  Anna Hart,et al.  Mann-Whitney test is not just a test of medians: differences in spread can be important , 2001, BMJ : British Medical Journal.

[21]  Jack E. Edwards,et al.  Involvement, Ability, Performance, and Satisfaction as Predictors of College Attrition , 1982 .

[22]  Francisco José García-Peñalvo,et al.  Key factors for determining student satisfaction in engineering: a regression study , 2014 .

[23]  Matthew W. Ohland,et al.  Climate in undergraduate engineering education from 1995 to 2009 , 2010, 2010 IEEE Frontiers in Education Conference (FIE).

[24]  Brandon I. Collier-Reed,et al.  Why students leave engineering and built environment programmes when they are academically eligible to continue , 2015 .

[25]  Neil A. Morgan,et al.  The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Business Performance , 2006 .

[26]  Ya-hui Su,et al.  The constitution of agency in developing lifelong learning ability: the ‘being’ mode , 2011 .

[27]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[28]  Norman D. Aitken College Student Performance, Satisfaction and Retention: Specification and Estimation of a Structural Model. , 1982 .

[29]  Barbara Kerr,et al.  The flipped classroom in engineering education: A survey of the research , 2015, 2015 International Conference on Interactive Collaborative Learning (ICL).

[30]  Diane Hamilton,et al.  Factors Affecting Student Performance and Satisfaction: Online versus Traditional Course Delivery , 2005, J. Comput. Inf. Syst..

[31]  Dirk Ifenthaler,et al.  Preparing the Next Generation of Education Researchers for Big Data in Higher Education , 2017 .

[32]  Jørn Hetland,et al.  School-Related Social Support and Students' Perceived Life Satisfaction , 2009 .

[33]  P. Alexander The Development of Expertise: The Journey From Acclimation to Proficiency , 2003 .

[34]  Brigitte Maier,et al.  Students' expectations of, and experiences in e-learning: Their relation to learning achievements and course satisfaction , 2010, Comput. Educ..

[35]  Jaeho Choi,et al.  A structural equation model of predictors of online learning retention , 2013, Internet High. Educ..

[36]  Laura J. Shepherd,et al.  Gender and cultural bias in student evaluations: Why representation matters , 2019, PloS one.

[37]  K. Elliott,et al.  Student Satisfaction: An alternative approach to assessing this important concept , 2002 .

[38]  J. Sinacore,et al.  A Comparative Study of Seven Measures of Patient Satisfaction , 1995, Medical care.

[39]  R. Pekrun,et al.  Emotions and Motivation in Learning and Performance , 2014 .

[40]  N. Denzin The research act: A theoretical introduction to sociological methods , 1977 .

[41]  D. Bolliger Key Factors for Determining Student Satisfaction in Online Courses , 2004 .

[42]  John T. E. Richardson,et al.  The National Student Survey: development, findings and implications , 2007 .

[43]  Celina Pinto Leão,et al.  Is students' satisfaction in electrical engineering courses influenced by gender? , 2017, 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC).

[44]  F. Dochy,et al.  Using student-centred learning environments to stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness , 2010 .

[45]  Meera Komarraju,et al.  Increased Career Self-Efficacy Predicts College Students’ Motivation, and Course and Major Satisfaction , 2014 .

[46]  Päivi Häkkinen,et al.  Student agency analytics: learning analytics as a tool for analysing student agency in higher education , 2020, Behav. Inf. Technol..

[47]  B. Browne,et al.  Student as Customer: Factors Affecting Satisfaction and Assessments of Institutional Quality , 1998 .