Education · Instructional design · Uncategorized

Evaluating and Identifying Online Resources



The second week of the Learning Theories and Instruction course covered the topics of the brain and learning, how the brain processes information, and how we solve problems.

For the second assignment, I am going to comment on some very interesting academic journal articles about brain research and learning, information processing in the brain, and problem-solving. I will discuss two articles for each of the three topics.

The first article about brain research and learning is titled Formal Learning Theory Dissociates Brain Regions with Different Temporal Integration by Jan Glascher and Christian Buchel. The researchers created an experiment using fMRI (Functional magnetic resonance imaging) to study Pavlovian fear conditioning where reinforcement incidents were fully varied during the experimental course. The researchers used the formal learning theory (Rescorla-Wagner model) as their theoretical framework. Using the fMRI and this theoretical perspective, they found that learning-related brain regions, such as the amygdala, which is involved in long temporal integration, and the higher perceptual regions, which are involved in short-term integration, such as the fusiform face area, and the parahippocampal place area. This research permits investigating changes related to areas activated during learning in the brain, as it shows that there are brain areas that differ in integrating past learning experiences by either calculating long-term outcome predictions or instantaneous reinforcement expectancies (Glascher, & Buchel, 2005).

The second article about brain research and learning is titled Brain Research, Learning, and Emotions: implications for education research, policy, and practice by Christina Hinton, Koji Miyamoto, and Bruno Della-Chiesa. The authors discuss implications for policy recommendations informed by brain research in the area of educational neuroscience. The first recommendation is to focus on the learning environment. The learning environment influences brain development greatly, and the authors propose a change in policy from focusing on the individual to focusing on restructuring the environment. The second recommendation entails making use of formative assessment. The brain is dynamic and constructed over time, and formative assessment is a powerful tool for guiding the development of abilities (Hinton, Miyamoto, & Della-Chiesa, 2008). The third recommendation is taking into account the importance of emotions. Emotion is essential to learning, and brain research suggests that schools should have positive learning environments that motivate students, and teachers should be trained in teaching students how to regulate their emotions, which is an important aspect of learning (Hinton, Miyamoto, & Della-Chiesa, 2008). The fourth is considering sensitive periods for language learning. Early foreign language instruction assists the brain in more efficiently and effectively learning its accent and grammar; starting instruction in pre-primary or primary school is advantageous (Hinton, Miyamoto, & Della-Chiesa, 2008). The fifth is informing reading instruction with neuroscience findings; a balanced approach to literacy informed by the dual importance of phonological and direct semantic processing in the brain is the most effective way to teach non-shallow alphabetic languages (Hinton, Miyamoto, & Della-Chiesa, 2008). The last is informing math instruction with neuroscience findings; because number and space are tightly linked in the brain, instructional methods that link these are powerful teaching tools (Hinton, Miyamoto, & Della-Chiesa, 2008).

The first article about information processing is titled Factors influencing the use of cognitive tools in web-based learning environments: A case study by Erol Ozcelik and Soner Yildirim. The authors found that students use Web-based cognitive tools for various cognitive tasks; cognitive tools have tremendous promise in empowering learners in Web-based learning environments (Ozcelik, & Yildirim, 2005). The authors have the following recommendations for designing and using cognitive tools for Web-based learning environments: 1) Provide an orientation for the tools that will be used; 2) the cognitive tools should be easy to use; 3) activities in instruction should demand using higher-order thinking; and, 4) internet connections should be more secure, stable, and fast (Ozcelik, & Yildirim, 2005).

The second article about information processing is titled Multimedia, Information Complexity and Cognitive Processing by Hayward P. Andres. The study shows a causal model of how multimedia and information complexity interact to influence sustained attention, mental effort and information processing quality, which all have an impact on comprehension and learner confidence and satisfaction outcomes (Andres, 2004). The results of the study show that “presentation media, or format, directly impacted sustained attention, mental effort, information processing quality, comprehension, learner confidence and satisfaction; information complexity had direct effects on sustained attention, mental effort, and information processing quality; and comprehension and learner confidence and satisfaction were influenced through a mediating sequence of sustained attention, mental effort, and information processing quality” (Andres, 2004).

The first article about problem-solving is titled The Relationship between Creative Cognition and Problem Solving by Serhat Arslan, Yunus Akdeniz, and Dilek Unal. The study’s aim is to search for the relationship between problem solving and creative cognition, and the results show that problem solving is positively related to creative cognition (Arslan, Akdeniz, & Unal, 2016).

The second article about problem-solving is titled Problem Solving as an Encoding Task: A Special Case of the Generation Effect by Jasmin M. Kizilirmak, Berit Wiegemann, and Alan Richardson-Klavehn. The authors found that the generation effect for CRAT, in which a new solution has to constructed based on existing knowledge rather than retrieving the solution from existing knowledge, shows a distinctive pattern of memory performance depending on the type of test, for example, solving old items, or indirect test, versus recognizing old items or solutions, or direct test (Kizilirmak, Wiegemann, & Richardson-Klavehn, 2016).



Andres, H. P. (2004). Multimedia, information complexity and cognitive processing. Information  Resources Management Journal, 17(1), pp. 63-78. Retrieved from

Arslan, S., Akdeniz, Y., & Unal, D. (2016). The relationship between creative cognition and  problem solving. Multidisciplinary Academic Conference. Retrieved from

Glascher, J., & Buchel, C. (2005). Formal learning theory dissociates brain regions with different temporal integration. Neuron, 47, pp. 295-306. Retrieved from

Hinton, C., Miyamoto, K., & Della-Chiesa, B. (2008). Brain research, learning and emotions: implications for education research, policy, and practice. European Journal of Education, 43(1). Retrieved from

Kizilirmak, J. M., Wiegemann, B., & Richardson-Klavehn, A. (2016). Problem solving as an  encoding task: A special case of the generation effect. Journal of Problem Solving, 9. Retrieved from

Ozcelik, E., & Yildirim, S. (2005). Factors influencing the use of cognitive tools in Web-based learning environments: A case study. The Quarterly Review of Distance Education, 6(4), 295-308. Retrieved from




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