Identifying Patterns of Learner Behaviour: What Business Statistics Students Do with Learning Resources

The interactions of early stage business students with learning resources over the duration of an introductory statistics module were analysed using latent class analysis. Four distinct behavioural groups were identified. While differing levels of face-to-face attendance and online interaction existed, all four groups failed to engage with online material in a timely manner. The four groups were found to demonstrate significantly different levels of attainment of the module learning outcomes. The patterns of behaviour of the different groups of students give insights as to which analytics education learning resources students use and how their use patterns relate to their level of attainment of the module learning outcomes.

[1]  R. Yilmaz Mustafa.  The Challenge of Teaching Statistics to Non-Specialists , 1996 .

[2]  Effective Practice in a Digital Age , 2009 .

[3]  L. A. Goodman.  Exploratory latent structure analysis using both identifiable and unidentifiable models , 1974 .

[4]  P. Taylor,et al.  Changing expectations: Preparing students for flexible learning , 2000 .

[5]  George Siemens,et al.  The Cambridge Handbook of the Learning Sciences: Educational Data Mining and Learning Analytics , 2014 .

[6]  S. Schwartz,et al.  Leaving College: Rethinking the Causes and Cures of Student Attrition , 1987 .

[7]  Ryan S. Baker,et al.  Educational Data Mining and Learning Analytics , 2014 .

[8]  Lynn G. McAlevey,et al.  Making statistics more effective for business? , 2001 .

[9]  Alfred Bork,et al.  Multimedia in Learning , 2001 .

[10]  John O’Donoghue,et al.  Mathematical under-preparedness: the influence of the pre-tertiary mathematics experience on students’ ability to make a successful transition to tertiary level mathematics courses in Ireland , 2007 .

[11]  Xin Chen,et al.  Mining Social Media Data for Understanding Students’ Learning Experiences , 2014, IEEE Transactions on Learning Technologies.

[12]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[13]  Ernest T. Pascarella,et al.  How College Affects Students: A Third Decade of Research. Volume 2. , 2005 .

[14]  Thomas Brendan Murphy,et al.  BayesLCA: An R Package for Bayesian Latent Class Analysis , 2014 .

[15]  Engaging Business Students in Quantitative Skills Development , 2015 .

[16]  Rebecca Ferguson,et al.  Innovating Pedagogy 2013 , 2013 .

[17]  R. Mayer,et al.  Nine Ways to Reduce Cognitive Load in Multimedia Learning , 2003 .

[18]  J. Biggs.  What the student does: teaching for enhanced learning , 1999 .

[19]  M. Hayat.  Guidelines for Assessment and Instruction in Statistics Education (GAISE): extending GAISE into nursing education. , 2014, The Journal of nursing education.

[20]  Matthew J. Koehler,et al.  What is Technological Pedagogical Content Knowledge (TPACK)? , 2013 .

[21]  Adrian E. Raftery,et al.  Model-based Methods of Classification: Using the mclust Software in Chemometrics , 2007 .

[22]  Brandon Muramatsu,et al.  Draft Standard for Learning Object Metadata , 2002 .

[23]  Christian Gütl,et al.  Ecosystem-based Theoretical Models for Learning in Environments of the 21st Century , 2008, Int. J. Emerg. Technol. Learn..

[24]  R. Mayer,et al.  Multimedia Learning: The Promise of Multimedia Learning , 2001 .

[25]  D. S. Moore,et al.  New Pedagogy and New Content: The Case of Statistics , 1997 .