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Graduate Discussion Rubric.html

Graduate Discussion Rubric

Overview

Your active participation in the discussions is essential to your overall success this term. Discussion questions will help you make meaningful connections between the course content and the larger concepts of the course. These discussions give you a chance to express your own thoughts, ask questions, and gain insight from your peers and instructor.

Directions

For each discussion, you must create one initial post and follow up with at least two response posts.

For your initial post, do the following:

• Write a post of 1 to 2 paragraphs.
• In Module One, complete your initial post by Thursday at 11:59 p.m. Eastern.
• In Modules Two through Ten, complete your initial post by Thursday at 11:59 p.m. of your local time zone.
• Consider content from other parts of the course where appropriate. Use proper citation methods for your discipline when referencing scholarly or popular sources.

For your response posts, do the following:

• Reply to at least two classmates outside of your own initial post thread.
• In Module One, complete your two response posts by Sunday at 11:59 p.m. Eastern.
• In Modules Two through Ten, complete your two response posts by Sunday at 11:59 p.m. of your local time zone.
• Demonstrate more depth and thought than saying things like “I agree” or “You are wrong.” Guidance is provided for you in the discussion prompt.

Discussion Rubric

Criteria	Exemplary	Proficient	Needs Improvement	Not Evident	Value
Comprehension	Develops an initial post with an organized, clear point of view or idea using rich and significant detail (100%)	Develops an initial post with a point of view or idea using appropriate detail (90%)	Develops an initial post with a point of view or idea but with some gaps in organization and detail (70%)	Does not develop an initial post with an organized point of view or idea (0%)	20
Timeliness	N/A	Submits initial post on time (100%)	Submits initial post one day late (70%)	Submits initial post two or more days late (0%)	10
Engagement	Provides relevant and meaningful response posts with clarifying explanation and detail (100%)	Provides relevant response posts with some explanation and detail (90%)	Provides somewhat relevant response posts with some explanation and detail (70%)	Provides response posts that are generic with little explanation or detail (0%)	20
Critical Thinking	Draws insightful conclusions that are thoroughly defended with evidence and examples (100%)	Draws informed conclusions that are justified with evidence (90%)	Draws logical conclusions (70%)	Does not draw logical conclusions (0%)	30
Writing (Mechanics)	Initial post and responses are easily understood, clear, and concise using proper citation methods where applicable with no errors in citations (100%)	Initial post and responses are easily understood using proper citation methods where applicable with few errors in citations (90%)	Initial post and responses are understandable using proper citation methods where applicable with a number of errors in citations (70%)	Initial post and responses are not understandable and do not use proper citation methods where applicable (0%)	20
Total:	100%

Module Overview19.html

Problem Solving

Solving problems is part of our everyday lives. Problem solving occurs when we must overcome an obstacle to achieve a goal (Sternberg & Sternberg, 2016). Problems come in many different forms. However, research on problem solving has created a problem-solving cycle that we often use to solve problems. You will learn about this cycle this week. Note that often what makes us successful or unsuccessful at problem solving is our ability to be flexible with steps. Flexibility means that we can move back and forth through the steps or even skip some of them. Continued monitoring of our progress will help us be more successful with problem solving. 

The process of decision making involves assessing choices. It also involves evaluating opportunities (Sternberg & Sternberg, 2016). It differs from problem solving. The goal of decision making is to draw conclusions about different situations or choices. Often, during the problem-solving process, you will make decisions to help you reach a solution. 

Consider the difference between well-structured and ill-structured problems. A well-structured problem is one that has a clear path to a solution. In contrast, in ill-structured problems, there is no clear path to the solution. In school, we are typically faced with well-structured problems. Then we are asked to solve these problems based on formulas we have learned. However, education has begun to explore the effectiveness of using ill-structured problems. The goal is to increase critical thinking and creative problem-solving skills. This approach to education is sometimes termed problem-based learning (PBL).

Creativity can also influence our ability to solve ill-structured problems. When solving ill-structured problems, individuals often have an “aha” or insight moment. Ill-structured problems require an individual to view the problem in a novel way. Then they experience insight to reach a solution. These are termed insight problems. Insight problems typically require individuals to use knowledge that is well known to them. In some cases, problem solving is facilitated by taking time off from thinking about the problem.

This week, you will learn about different factors that influence creative abilities. Additionally, you will examine literature on the neuroscience of creativity. These readings support the idea that creativity is a controlled cognitive process. The insight we feel when solving ill-structured or insight problems might seem to develop rapidly and spontaneously. However, it often develops gradually (Sternberg & Sternberg, 2016) and using planning.

Decision making, like problem solving, can be influenced by several factors. One such factor that influences our decisions is heuristics. Heuristics are strategies we use to make decisions. A given heuristic ignores some information to make a decision more quickly and accurately (Gigerenzer & Gaissmaier, 2011). Heuristics allow us to take shortcuts to make decisions. In contrast, logical reasoning would adhere to a strict set of principles that leads to correct reasoning. Many different heuristics exist and influence decision making in different ways. Heuristics are often used to help us make decisions. They can lead to individuals making poor decisions because, in using heuristics, we ignore some information.

Another type of thinking that helps us draw conclusions about situations is reasoning. Reasoning involves drawing conclusions based on evidence. It is often divided into deductive reasoning and inductive reasoning. Deductive reasoning allows us to logically reach certain conclusions about something based on certain conditions or rules. Like many cognitive processes examined throughout the course, reasoning is affected by emotion.

Chin and Chia (2006) describe that in PBL, students are given an ill-structured problem to work on, in many cases in collaborative groups. As the students work on the problem, the teacher acts as a coach, helping them to develop ideas and organize the knowledge they have to solve the problem. The teacher uses principles of scaffolding from Vygotsky (1978) to help students develop ideas related to the problem. In many cases, teachers or instructors will try to place the problem within a real-life scenario. This helps to increase a student’s ability to connect what they learn to real-world situations and applications. Consider an investigation using ill-structured problems and PBL in a biology course. Chin and Chia (2006) found that ill-structured problems and PBL learning helped to evoke cognitive processes. These included formulating a research problem and making comparisons. It also included applying knowledge to new situations, making decisions, and monitoring progress. There are cognitive benefits of ill-structured problem-solving activities. However, implementing this strategy in traditional K–12 learning environments can prove challenging. Educators juggle large class sizes and time limitations on classes in schools. They also face mandates to take part in high-stakes testing that informs teacher evaluation and school funding. 

Once an individual solves an insight problem, they have an “aha” experience (Batchelder & Alexander, 2012). Experiencing this “aha” or insight is often looked at as a measure of an individual’s creativity. In some views, it is considered an unconscious process, yet this has not been confirmed by research (Beaty et al., 2014). Beaty, Nusbaum, and Silvia examined this connection (2014). They found no connection between performance on insight problems and self-reported creativity. However, they did find evidence for a connection between intelligence and creativity. The authors suggest that creative thinking is more likely to be controlled by strategic cognitive thought processes than unconscious thought processes.

Gigerenzer and Gaissmaier reviewed heuristics in comparisons to other decision-making strategies such as logic or statistics (2011). They noted that heuristics have often been associated with errors. However, they are equal to statistics in helping individuals make decisions. They contend that experience will help us to select the proper heuristic. They also argue that the success of a heuristic will in part be determined by the structure of the environment. For example, one heuristic we use is the take-the-best heuristic. In this heuristic, we choose between options based on which option has the highest value. For example, if you were choosing between two paths to take to work, you might look for environmental cues that signal which path will be the best option. To do so, you might consider the time of day, the weather, traffic accidents, and so on. In examining these cues, you will see that they are related. For example, bad weather may lead to more traffic accidents on a certain path. Or the time of day might influence what the weather is like. In this case, then, the information is highly redundant. Consider cases where information is redundant. Research has illustrated that the take-the-best heuristic is just as accurate as statistical methods that make decisions based on integration of all cues (Dieckmann & Rieskamp, 2007). The environment of the decision-making process can influence how successful our decisions are. 

For instance, consider these premises: “If the heart stops beating, blood flow to the brain ceases” and “John’s heart stopped beating.” One can conclude with logical certainty that blood flow to John’s brain will cease. Thus, deductive arguments can be assessed as logically valid or invalid (Blanchette & Leese, 2011, p. 235). In contrast, inductive reasoning involves making conclusions based on observations or facts that are likely to explain the situation. Here, we are never able to reach a logically certain conclusion (Sternberg & Sternberg, 2016). 

Blanchette and Leese (2011) explored the relationship between deductive reasoning and emotion. They looked at changes in skin conductance. Skin conductance is a measure of the amount of moisture on the skin produced by sweat. It is considered a measure of arousal of the sympathetic nervous system. Their experiments illustrated that negative emotions led to greater increases in skin conductance. This also correlated with decreased logical reasoning. In this case, then, higher activity of the sympathetic nervous system is associated with poorer decisions. Their experiments help bring us one step closer to understanding how changes in the body influence cognitive processes. The current experiments suggest that increased arousal from negative emotions leads to decreased reasoning skills.

References

Batchelder, W. H., & Alexander, G. E. (2012). Insight problem solving: A critical examination of the possibility of formal theory. Journal of Problem Solving, 5(1), 56–100.

Beaty, R. E., Nusbaum, E. C., & Silvia, P. J. (2014). Does insight problem solving predict real-world creativity? Psychology of Aesthetics, Creativity, and the Arts, 8(3), 287–292.

Blanchette, I., & Leese, J. (2011). The effect of negative emotion on deductive reasoning: Examining the contribution of physiological arousal. Experimental Psychology, 58(3), 235–246.

Chin, C., & Chia, L. (2006). Problem-based learning: Using Ill-structured problems in biology project work. Science Education, 90(1), 44–67.

Dieckmann A., & Rieskamp J. (2007). The influence of information redundancy on probabilistic inferences. Memory and Cognition, 35(7), 1801–1813.

Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–482.

Sternberg, R. J., & Sternberg, K. (2016). Cognitive psychology (7th ed.). Belmont, CA: Wadsworth.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.