Student Engagement with a Science Simulation: Aspects that Matter

It is argued that multimedia technology affords an opportunity to better visualise complex relationships often seen in chemistry. This paper describes the influence of chemistry simulation design facets on user progress through a simulation. Three versions of an acid-base titration simulation were randomly allocated to 36 volunteers to examine their interactions with the simulation. The impact of design alterations on the total number of interactions and their patterns was analysed for the following factors: (a) the place of a feature on the screen, (b) alignment of the sequence of instructions, (c) additional instruction before the simulation, (d) interactivity of a feature. Additionally, interactions between individual factors, such as age, prior experience with science simulations and computer games, perception of the difficulty of science simulations, and general subject knowledge, on one hand, and the efficiency of using the simulation, on the other hand, were examined. The findings suggest that: (a) centrality of the position of an element significantly affects the number of interactions with the element, (b) re-arranging the sequence of instructions on the screen in left-to-right order improves the following of instructions, (c) providing users with additional written advice to follow numbered instructions does not have a significant impact on student behaviour, (d) interactivity of a feature was found to have a strong positive correlation with the number of interactions with that feature, which warrants a caution about unnecessary interactivity that may hinder simulation efficiency. Surprisingly, neither prior knowledge of chemistry nor the age of the participants had a significant effect on either the number of interactions or the ability to follow on-screen instructions. (DIPF/Orig.)

[1]  D. Polkinghorne.  Narrative Knowing and the Human Sciences , 2010 .

[2]  Ingo Eilks,et al.  Using Multimedia Learning Aids from the Internet for Teaching Chemistry: Not as Easy as it Seems? , 2010 .

[3]  Rex A. Thomas,et al.  Simulations: An Opportunity We Are Missing. , 1991 .

[4]  R. Moreno,et al.  Does the modality principle hold for different media? A test of the method-affects-learning hypothesis , 2006, J. Comput. Assist. Learn..

[5]  Richard E. Mayer,et al.  e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning , 2002 .

[6]  S. Rodrigues,et al.  Questioning Chemistry: The Role of Level, Familiarity, Language and Taxonomy , 2010 .

[7]  Maggie McPherson,et al.  A New Generation Gap? Some thoughts on the consequences of increasingly early ICT first contact , 2005 .

[8]  Amy Pallant,et al.  Reasoning with Atomic-Scale Molecular Dynamic Models , 2004 .

[9]  David Richard Moore,et al.  E-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning , 2006 .

[10]  Bruce C. Howard,et al.  The Influence of Metacognitive Skills on Learners' Memory of Information in a Hypermedia Environment , 2004 .

[11]  Robert K. Atkinson,et al.  Fostering multimedia learning of science: Exploring the role of an animated agent's image , 2007, Comput. Educ..

[12]  R. Azevedo.  Using Hypermedia as a Metacognitive Tool for Enhancing Student Learning? The Role of Self-Regulated Learning , 2005 .

[13]  Italo Testa,et al.  Students' reading images in kinematics: The case of real-time graphs , 2002 .

[14]  S. Hennessy,et al.  Research into teaching with whole‐class interactive technologies , 2010 .

[15]  A. Segall.  Revisiting pedagogical content knowledge: the pedagogy of content/the content of pedagogy , 2004 .

[16]  Richard Lowe,et al.  Learning with Animation: Research Implications for Design , 2007 .

[17]  P. Chandler,et al.  Cognitive Load Theory and the Format of Instruction , 1991 .

[18]  Philip Barker.  Re‐evaluating a model of learning design , 2008 .

[19]  Johan van Braak,et al.  A multidimensional approach to determinants of computer use in primary education: teacher and school characteristics , 2008, J. Comput. Assist. Learn..

[20]  Patricia D. Mautone,et al.  Social cues in multimedia learning: Role of speaker's voice. , 2003 .

[21]  M. Bannert,et al.  Construction and interference in learning from multiple representation , 2003 .

[22]  Hans van der Meij,et al.  Email use in elementary school: an analysis of exchange patterns and content , 2002, Br. J. Educ. Technol..

[23]  Hector Garcia-Rodicio,et al.  The use of modality in the design of verbal aids in computer-based learning environments , 2008, Interact. Comput..

[24]  Barry Smyth,et al.  Mobile information access: A study of emerging search behavior on the mobile Internet , 2007, TWEB.

[25]  Robert J. Beichner,et al.  THE EFFECT OF SIMULTANEOUS MOTION PRESENTATION AND GRAPH GENERATION IN A KINEMATICS LAB , 1990 .

[26]  Paul Ginns.  Meta-Analysis of the Modality Effect. , 2005 .

[27]  Sara de Freitas,et al.  A Model of Motivation for Virtual-Worlds Avatars , 2008, IVA.

[28]  D. Thompson,et al.  Virtual Worlds for Science Learning , 2010 .

[29]  P. Chandler,et al.  THE SPLIT‐ATTENTION EFFECT AS A FACTOR IN THE DESIGN OF INSTRUCTION , 1992 .

[30]  J. Gosby.  MEDIA REVIEWS: Basics of Qualitative Research - Techniques and Procedures for Developing Grounded Theory 2nd Edition by A. Strauss and J. Corbin. Sage Publications, , 2000 .

[31]  Heather Brasell,et al.  The effect of real‐time laboratory graphing on learning graphic representations of distance and velocity , 1987 .