Virtual Manipulation: An On-Screen Interactive Visual Design for Science Learning
01 Jan 2016-pp 299-308
TL;DR: This research attempts to explore the effectiveness of enactment by manipulating virtual features in interactive visualization when compared with visual animation in virtual manipulation condition where student perform virtually in the on-screen object better than animation in respect to various learning outcome.
Abstract: Interactivity in e-learning environment is an innovative approach in teaching-learning. Predominantly theoretical justification of interactive learning environment has been discussed on the basis of the process of visual and auditory information in the memory system emphasizing the result oriented perspective in the sense that they have given importance on computer response to learner action rather than learner activity and engagement in computer programming. However, by definition interactivity is described as ‘to act’. In this view point the present research attempts to explore the effectiveness of enactment by manipulating virtual features in interactive visualization when compared with visual animation. To investigate the effectiveness of different visual condition researchers have developed two different types of instructional module (interactive virtual manipulation and animated visual). Total 360 students have been selected to implement the study with different matching criteria. MANOVA is conducted to find out the group difference in different condition. Result showed a momentous mean difference in different condition i.e., in virtual manipulation (execution of action) condition where student perform virtually in the on-screen object better than animation (observed action) in respect to various learning outcome. Result is discussed critically from several theoretical focal points.
References
More filters
1,687 citations
TL;DR: This paper showed that learners who were allowed to exercise control over the pace of the narrated animation across two presentations (part-part presentation) performed better on transfer but not retention tests compared with learners who received the same 2 presentations at normal speed without any learner control (whole-whole presentation).
Abstract: In 2 experiments, students received 2 presentations of a narrated animation that explained how lightning forms followed by retention and transfer tests. In Experiment 1, learners who were allowed to exercise control over the pace of the narrated animation before a second presentation of the same material at normal speed (part-whole presentation) performed better on transfer but not retention tests compared with learners who received the same 2 presentations in the reverse order (whole-part presentation). In Experiment 2, learners who were allowed to exercise control over the pace of the narrated animation across 2 presentations (part-part presentation) performed better on transfer but not retention tests compared with learners who received the same 2 presentations at normal speed without any learner control (whole-whole presentation). These results are consistent with cognitive load theory and a 2-stage theory of mental model construction.
697 citations
TL;DR: Manipulating children's gesture during instruction in a new mathematical concept found that requiring children to gesture while learning the new concept helped them retain the knowledge they had gained during instruction and had no effect on solidifying learning.
436 citations
Book•
01 Jan 2006TL;DR: In this paper, the authors introduce the concept of location descriptors and define a set of metrics for evaluating the quality of a location's descriptors, such as: Situation-Specific Maximum Dispersion (SMP), Bivariate Relationships, and Bivariate Normality.
Abstract: Part I Introductory Terms and Concepts. Definitions of Some Basic Terms. Levels of Scale. Some Experimental Design Considerations. Some Key Concepts. Reflection Problems. Part II Location. Reasonable Expectations for Statistics. Location Concepts. Three Classical Location Descriptive Statistics. Four Criteria for Evaluating Statistics. Two Robust Location Statistics. Some Key Concepts. Reflection Problems. Part III Dispersion.Quality of Location Descriptive Statistics. Important in Its Own Right. Measures of Score Spread. Variance. Situation-Specific Maximum Dispersion. Robust Dispersion Descriptive Statistics. Standardized Score World. Some Key Concepts. Reflection Problems. Part IV Shape. Two Shape Descriptive Statistics. Normal Distributions. Two Additional Univariate Graphics. Some Key Concepts. Reflection Problems. Part V Bivariate Relationships. Pearson's r. Three Features of r. Three Interpretation Contextual Factors. Psychometrics of the Pearson r. Spearman's rho. Two Other r -Equivalent Correlation Coefficients. Bivariate Normality. Some Key Concepts. Reflection Problems. Part VI Statistical Significance. Sampling Distributions. Hypothesis Testing. Properties of Sampling Distributions. Standard Error/Sampling Error. Test Statistics. Statistical Precision and Power. pCALCULATED. Some Key Concepts. Reflection Problems. Part VII Practical Significance. Effect Sizes. Confidence Intervals. Confidence Intervals for Effect Sizes. Some Key Concepts. Reflection Problems. Part VIII Multiple Regression Analysis: Basic GLM Concepts. Purposes of Regression. Simple Linear Prediction. Case #1: Perfectly Uncorrelated Predictors. Case #2: Correlated Predictors, No Suppressor. Effects. Case #3: Correlated Predictors, Suppressor. Effects Present. b Weights versus Structure Coefficients. A Final Comment on Collinearity. Some Key Concepts. Reflection Problems. Part IX A GLM Interpretation Rubric. Do I Have Anything?Where Does My Something Originate? Stepwise Methods. Invoking Some Alternative Models. Some Key Concepts. Reflection Problems. Part X One-way Analysis of Variance (ANOVA). Experimentwise Type I Error. ANOVA Terminology. The Logic of Analysis of Variance. Practical and Statistical Significance. The "Homogeneity of Variance" Assumption. Post Hoc Tests. Some Key Concepts. Reflection Problems. Part XI Multiway and Alternative ANOVA Models. Multiway Models. Factorial versus Nonfactorial Analyses. Fixed-, Random-, and Mixed-Effects Models. Brief Comment on ANCOVA. Some Key Concepts. Reflection Problems. Part XII The General Linear Model (GLM): ANOVA via Regression. Planned Contrasts. Trend/Polynomial Planned Contrasts. Repeated Measures ANOVA via Regression. GLM Lessons. Some Key Concepts. Reflection Problems. Part XIII Some Logistic Models: Model Fitting in a Logistic Context. Logistic Regression. Loglinear Analysis. Some Key Concepts. Reflection Problems. Appendix: Scores (n = 100) with Near Normal Distributions.
434 citations
TL;DR: In this paper, the authors show that participants used the interactive features like stopping, replaying, reversing or changing speed to adapt the pace of the video demonstration, which led to an uneven distribution of their attention and cognitive resources across the videos, which was more pronounced for difficult knots.
421 citations