Personalizing Persuasive Educational Technologies to Learners’ Cognitive Ability

Persuasive Educational Technology (PET) is an effective tool for engaging students and promoting learning, using various persuasive strategies. Research has shown that personalizing PET could increase their effectiveness at engaging students and promoting learning. Cognitive ability has been identified as an important factor that could lead to individual differences in learning. Yet, there is a gap in knowledge on how learners’ ability on various cognitive dimensions identified by Bloom’s Taxonomy, is associated with their susceptibility to distinct persuasive strategies often used in PETs designs. To fill this gap, we conducted a large-scale study of 402 students to investigate how their cognitive ability scores relate to their susceptibility to persuasive strategies (Reward, Social Learning, and Trustworthiness) that are commonly used in PET design. We assessed the Cognitive ability scores using the test questions adapted from the Educational Testing Service (ETS) Kit. Our results show that there is a relationship between individuals’ cognitive abilities and their susceptibility to persuasive strategies. People with high ability to Remember and Evaluate concepts are more susceptible to the Reward Strategy, while those high in ability to Understand are positively associated with the Trustworthiness Strategy. Similarly, we uncovered that individuals high in the ability to Apply, Analyze, and Synthesize ideas are positively associated with the Social Learning Strategy. These findings can guide PET designers on how to develop PET tailored to learners belonging to different cognitive ability dimensions.

[1]  Sara Jones,et al.  A Persuasive Social Actor for Activity Awareness in Learning Groups , 2016, BCS HCI.

[2]  Andrés Lucero,et al.  Persuasive Technologies in Education: Improving Motivation to Read and Write for Children , 2006, PERSUASIVE.

[3]  J. Caplan,et al.  High Reward Makes Items Easier to Remember, but Harder to Bind to a New Temporal Context , 2012, Front. Integr. Neurosci..

[4]  Sandra Burri Gram-Hansen,et al.  Persuasive Technology for Learning and Teaching – The EuroPLOT Project , 2013 .

[5]  Kiemute Oyibo,et al.  The Influence of Cognitive Ability on the Susceptibility to Persuasive Strategies , 2018, PPT@PERSUASIVE.

[6]  A Alexander Beaujean,et al.  Invariance in the Reynolds Intellectual Assessment Scales for Black and White referred students. , 2014, Psychological assessment.

[7]  Md. Solaiman Jony.  Student Centered Instruction for Interactive and Effective Teaching Learning: Perceptions of Teachers in Bangladesh , 2016 .

[8]  Kiemute Oyibo,et al.  Investigation of the Persuasiveness of Social Influence in Persuasive Technology and the Effect of Age and Gender , 2017, PPT@PERSUASIVE.

[9]  Kiemute Oyibo,et al.  Investigation of the Influence of Personality Traits on Cialdini's Persuasive Strategies , 2017, PPT@PERSUASIVE.

[10]  Morten Aagaard,et al.  The Application of Persuasive Technology to educational settings: Some theoretical from the HANDS Project , 2010 .

[11]  Marley W. Watkins,et al.  An Exploratory Investigation of the Factor Structure of the Reynolds Intellectual Assessment Scales (RIAS) , 2009 .

[12]  B. J. Fogg,et al.  Persuasive technology: using computers to change what we think and do , 2002, UBIQ.

[13]  Adam W. McCrimmon,et al.  Review of the Wechsler Abbreviated Scale of Intelligence, Second Edition (WASI-II) , 2013 .

[14]  Brian Knutson,et al.  Reward-Motivated Learning: Mesolimbic Activation Precedes Memory Formation , 2006, Neuron.

[15]  Raddy L. Ramos,et al.  Reward expectation alters learning and memory: The impact of the amygdala on appetitive-driven behaviors , 2009, Behavioural Brain Research.

[16]  S. Thompson.  Social Learning Theory , 2008 .

[17]  I Wayan Widana,et al.  HIGHER ORDER THINKING SKILLS ASSESSMENT (HOTS) , 2017, JISAE: Journal of Indonesian Student Assessment and Evaluation.

[18]  Maithilee Kunda,et al.  Addressing the Raven's Progressive Matrices Test of "General" Intelligence , 2009, AAAI Fall Symposium: Multi-Representational Architectures for Human-Level Intelligence.

[19]  Marc Busch,et al.  Personalized Persuasive Technology - Development and Validation of Scales for Measuring Persuadability , 2013, PERSUASIVE.

[20]  Simin Ghavifekr,et al.  Teaching and Learning with Technology: Effectiveness of ICT Integration in Schools , 2015, Indonesian Journal of Educational Research and Technology.

[21]  Lykke Brogaard Bertel,et al.  Tema 2: The NAO robot as a Persuasive Educational and Entertainment Robot (PEER) – a case study on children’s articulation, categorization and interaction with a social robot for learning , 2015 .

[22]  Boris E. R. de Ruyter,et al.  Personalizing persuasive technologies: Explicit and implicit personalization using persuasion profiles , 2015, Int. J. Hum. Comput. Stud..

[23]  Rita Orji,et al.  Personalizing Persuasive Strategies in Gameful Systems to Gamification User Types , 2018, CHI.

[24]  Paul A. McDermott,et al.  Does Learning Behavior Augment Cognitive Ability as an Indicator of Academic Achievement , 2004 .

[25]  Rita Orji,et al.  Persuasion and Culture: Individualism-Collectivism and Susceptibility to Influence Strategies , 2016, PPT@PERSUASIVE.

[26]  J. Raven,et al.  Manual for Raven's progressive matrices and vocabulary scales , 1962 .

[27]  David Chan,et al.  Verbal overshadowing effects on Raven's advanced progressive matrices: Evidence for multidimensional performance determinants , 1995 .

[28]  Kiemute Oyibo,et al.  Investigation of Social Predictors of Competitive Behavior in Persuasive Technology , 2017, PERSUASIVE.

[29]  Ruth B. Ekstrom,et al.  Manual for kit of factor-referenced cognitive tests , 1976 .

[30]  Chrysanne Di Marco,et al.  Towards Personality-driven Persuasive Health Games and Gamified Systems , 2017, CHI.

[31]  Harri Oinas-Kukkonen,et al.  Persuasive Systems Design: Key Issues, Process Model, and System Features , 2009, Commun. Assoc. Inf. Syst..