Why Intellectus Accelerates Novices Learning Statistics

“How do I drive students to successful learning?” is a question confronting educators, from provosts, deans, department chairs, to faculty.  When combined with moving learning online, using technologies, and adding a challenging subject like statistics, driving students to success becomes an obstacle course. 

Intellectus Statistics AutoDrafting technology analyzes data and automatically generates a written interpretation of statistical output for students. The technology can help students and instructors navigate the learning and teaching of statistics by building bridges over knowledge gaps, accelerating learning by reducing cognitive loads for novices, and avoiding putting the brakes on learning with embedded support throughout the journey.

Scaffolding Designs for Novice Learners

Learning differs between novices and experts. Novices depend on concept formation, whereas experts use concept integration (Daley, 1999). Concept formation is accelerated by guiding learners through step-by-step tasks that illustrates and demonstrates how components of statistical analysis become integrated.    

Intellectus’ AutoDrafting technology facilitates concept formation through step-by-step guidance and examples of how to conduct and understand statistical analyses and interpretations. By comprehensively including the components of an analysis in the output, including automated interpreted assumptions, automated interpreted output in plain English narrative, automatically generating appropriate tables and figures, as well as presenting worked-through examples of how the findings and tables are reported.  Intellectus creates conceptual landmarks for the novice learner’s journey where there are gaps in students’ knowledge. Intellectus also provides context-sensitive help that can bridge the knowledge gaps and drive them to the solution. Together, these features allow novice students to form the concept of how data analyses are conducted, what they mean, and how they are written.

Managing Cognitive Loads

Being overwhelmed is a common obstacle for novices learning statistics. With so many new concepts, mixing with some less-than optimal math skills, novices cannot organize their learning into meaning chunks/concepts. According to Cognitive Load theory (Sweller, 1988), there are three types of cognitive load: intrinsic load (task complexity), extraneous load (features that are not beneficial for learning), and germane load (features that are beneficial for learning). Novice learners learn better from worked examples (Cooper & Sweller, 1987; Paas & Van Merriënboer, 1994; Sweller & Cooper, 1985) by reducing task complexity on intrinsic load. Intellectus reduces task complexity with a simple user-interface, useful default settings, and fully worked examples. Extraneous load is minimized by stripping away irrelevant options and unnecessary, confusing output; Intellectus only provides information relevant for presentation and understanding. Germane load is enhanced in Intellectus by providing instructional features such as scroll-overs that immediately explain statistical figures, terms, and symbols. Intellectus manages these cognitive load types in a way that optimizes cognitive resources.

Supporting Retention

Repetition and meaning are keys to long-term memory. For example, introduction to statistics terms just once is subject to primacy and recency effects, while seeing terms a third time increases the retention of structurally important information. Increased repetition allows for important information to be remembered (Bromage & Mayer, 1986).  Intellectus’ built-in datasets, textbook, and videos provide novices numerous complete examples. These examples produce and explain relevant terms and symbols that provide meaningful encoding and retrieval opportunities, and champions retention and understanding.

Synergizing Accelerators

Intellectus accelerates statistics learning by supporting students’ ability to form statistical concepts, managing cognitive load, and promoting retention opportunities. To learn more about Intellectus Statistics click here.

References

Bromage, B. K., & Mayer, R. E. (1986). Quantitative and qualitative effects of repetition on learning from technical text. Journal of Educational Psychology, 78(4), 271–278. https://doi.org/10.1037/0022-0663.78.4.271

Cooper, G., & Sweller, J. (1987). Effects of schema acquisition and rule automation on mathematical problem-solving transfer. Journal of Educational Psychology, 79, 347–362.

Daley, B.J. (1999). Novice to Expert: An Exploration of How Professionals Learn.  Sage Journals, Vol 49 (4), 133-147. https://doi.org/10.1177/074171369904900401

Paas, F., & Van Merriënboer, J. J. G. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach. Journal of Educational Psychology, 86, 122–133.

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognition Science, 12 (2), 257–285. https://doi.org/10.1207/s15516709cog1202_4

Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2, 59–89.