Learning Analytics to Inform Teaching and Learning Approaches

Learning analytics is an evolving discipline with capability for educational data analysis to enable better understanding of learning processes. This paper reports on learning analytics research at Institute of Technology Blanchardstown, Ireland, that indicated measureable factors can identify first year students at risk of failing based on data available prior to commencement of first year of study. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines (n=1,207). Data was gathered from both student enrolment data maintained by college administration, and an online, self-reporting tool administered during induction sessions for students enrolling into the first year of study. Factors considered included prior academic performance, personality, motivation, selfregulation, learning approaches, learner modality, age and gender. A k-NN classification model trained on data from the 2010 and 2011 student cohort, and tested on data from the 2012 student cohort correctly identified 74% of students at risk of failing. Some factors predictive of at-risk students are malleable, and relate to an effective learning disposition; specifically, factors relating to self-regulation and motivation. This paper discusses potential benefits of measurement of learner disposition.

[1]  B. Zimmerman.  Self-Regulated Learning and Academic Achievement: An Overview , 1990 .

[2]  George Siemens,et al.  Learning analytics and educational data mining: towards communication and collaboration , 2012, LAK.

[3]  P. Pintrich.  A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). , 1991 .

[4]  Eitel J. M. Lauría,et al.  Open academic analytics initiative: initial research findings , 2013, LAK '13.

[5]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[6]  Hendrik Drachsler,et al.  The pulse of learning analytics understandings and expectations from the stakeholders , 2012, LAK.

[7]  Kristy J. Lauver,et al.  Do psychosocial and study skill factors predict college outcomes? A meta-analysis. , 2004, Psychological bulletin.

[8]  Abbas Pourhosein Gilakjani.  A Match or Mismatch Between Learning Styles of the Learners and Teaching Styles of the Teachers , 2012 .

[9]  George Siemens,et al.  Learning analytics: envisioning a research discipline and a domain of practice , 2012, LAK.

[10]  R. B. Sachin,et al.  A Survey and Future Vision of Data Mining in Educational Field , 2012, 2012 Second International Conference on Advanced Computing & Communication Technologies.

[11]  Tim De Feyter,et al.  Unraveling the impact of the Big Five personality traits on academic performance: the moderating and mediating effects of self-efficacy and academic motivation , 2012 .

[12]  Austin Gibbons,et al.  Learning Analytics , 2014, Encyclopedia of Social Network Analysis and Mining.

[13]  Neal Schmitt,et al.  Alternatives to GPA 1 Prediction of Four-Year College Student Performance using Cognitive and Noncognitive Predictors and the Impact on Demographic Status of Admitted Students , 2009 .

[14]  M. Tavakol,et al.  Making sense of Cronbach's alpha , 2011, International journal of medical education.

[15]  Dragan Gasevic,et al.  Scientometrics as an Important Tool for the Growth of the Field of Learning Analytics , 2014 .

[16]  Christopher H. Achen.  Interpreting and Using Regression , 1982 .

[17]  T. Micceri.  The unicorn, the normal curve, and other improbable creatures. , 1989 .

[18]  D. Schunk.  Commentary on self-regulation in school contexts , 2005 .

[19]  L. R. Goldberg.  THE DEVELOPMENT OF MARKERS FOR THE BIG-FIVE FACTOR STRUCTURE , 1992 .

[20]  Eitel J. M. Lauría,et al.  Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative , 2014, J. Learn. Anal..

[21]  Oecd Ocde.  Education At A Glance , 2005 .

[22]  Z. Kablan.  The effect of manipulatives on mathematics achievement across different learning styles , 2016 .

[23]  H. V. D. Flier,et al.  Using multiple and specific criteria to assess the predictive validity of the Big Five personality factors on academic performance. , 2010 .

[24]  Simon Cassidy,et al.  Exploring individual differences as determining factors in student academic achievement in higher education , 2012 .

[25]  Maureen A. Conard,et al.  Aptitude is not enough: How personality and behavior predict academic performance. , 2006 .

[26]  J. Biggs,et al.  The revised two-factor Study Process Questionnaire: R-SPQ-2F. , 2001, The British journal of educational psychology.

[27]  N. Entwistle,et al.  Approaches to Learning and Levels of , 1979 .

[28]  Vivienne Patterson,et al.  Key facts and figures 2012/13 , 2014 .

[29]  Øyvind Lund Martinsen,et al.  Personality, approaches to learning and achievement , 2010 .

[30]  Geoff Holmes,et al.  Benchmarking Attribute Selection Techniques for Discrete Class Data Mining , 2003, IEEE Trans. Knowl. Data Eng..

[31]  Matthew D. Pistilli,et al.  Course signals at Purdue: using learning analytics to increase student success , 2012, LAK.

[32]  D. Duffin,et al.  Using ICT to enable inclusive teaching practices in higher education , 2009 .

[33]  M. Boekaerts.  Self-regulated Learning at the Junction of Cognition and Motivation , 1996 .

[34]  Adrian Furnham,et al.  A possible model for understanding the personality--intelligence interface. , 2004, British journal of psychology.

[35]  Craig S. Wells,et al.  Running Head : CLT AND SAMPLE SIZE Central Limit Theorem and Sample Size , 2006 .

[36]  Matthias Burisch,et al.  Test length and validity revisited , 1997 .

[37]  Åge Diseth,et al.  Self-efficacy, goal orientations and learning strategies as mediators between preceding and subsequent academic achievement , 2011 .

[38]  John A. Johnson,et al.  The international personality item pool and the future of public-domain personality measures ☆ , 2006 .

[39]  Geraldine Gray,et al.  A Review of Psychometric Data Analysis and Applications in Modelling of Academic Achievement in Tertiary Education , 2014, J. Learn. Anal..

[40]  Jeffrey R. Harring,et al.  Reexamining the Impact of Nonnormality in Two-Group Comparison Procedures , 2015 .

[41]  Barbara Joy Miller-Reilly.  Affective change in adult students in second chance mathematics courses : three different teaching approaches , 2006 .

[42]  Ulrik Schroeder,et al.  A reference model for learning analytics , 2012 .

[43]  N. Entwistle.  Contrasting Perspectives on Learning , 2005 .

[44]  A. Furnham,et al.  Personality, intelligence and approaches to learning as predictors of academic performance , 2008 .

[45]  Dawn P. Flanagan,et al.  Interpreting Intelligence Tests from Contemporary Gf-Gc Theory: Joint Confirmatory Factor Analysis of the WJ-R and KAIT in a Non-White Sample , 1998 .

[46]  H. Kundel,et al.  Measurement of observer agreement. , 2003, Radiology.

[47]  R. Sternberg,et al.  Intelligence as Developing Expertise. , 1999, Contemporary educational psychology.

[48]  Fielding I. Winters,et al.  Self-Regulation of Learning within Computer-based Learning Environments: A Critical Analysis , 2008 .

[49]  Kevin Downing,et al.  The reciprocal relationship between motivation and self-regulation: A longitudinal study on academic performance , 2010 .

[50]  Simon Buckingham Shum,et al.  Epistemology, pedagogy, assessment and learning analytics , 2013, LAK '13.

[51]  James C. Kaufman,et al.  The role of personality and motivation in predicting early college academic success in non-traditional students at a Hispanic-serving institution , 2008 .

[52]  M. Covington.  Goal theory, motivation, and school achievement: an integrative review. , 2000, Annual review of psychology.

[53]  M. Galesic,et al.  Effects of Questionnaire Length on Participation and Indicators of Response Quality in a Web Survey , 2009 .

[54]  Oliver Mooney,et al.  A study of progression in Irish higher education , 2010 .

[55]  César Hervás-Martínez,et al.  Data Mining Algorithms to Classify Students , 2008, EDM.

[56]  Radek Pelánek,et al.  Metrics for Evaluation of Student Models , 2015, EDM.

[57]  Timothy J. Pleskac,et al.  Prediction of 4-year college student performance using cognitive and noncognitive predictors and the impact on demographic status of admitted students. , 2009, The Journal of applied psychology.

[58]  M. Hofmann,et al.  Learning Factor Models of Students at Risk of Failing in the Early Stage of Tertiary Education , 2016, J. Learn. Anal..