Wellness: Good Stress: Exercise for Brain Health

Like many people, I used to struggle to keep a consistent exercise routine. Armed with nothing but a vague sense that it was good for me and usually extra motivation to lose weight, I’d start something only to discard it in a couple of days or weeks. This cycle changed when my vague sense developed into a concrete understanding of how exercise improves almost every aspect of my life. Now, I’ve been exercising consistently for 2.5 years, and it started when I found out how exercise benefits the brain.

The benefits of exercise on the brain include improving learning, stress management, anxiety, mood, and focus. These effects are so robust that people commonly stop taking medications for anxiety, depression, and ADHD when they start exercising consistently. The mechanisms for this are detailed in Spark: The Revolutionary New Science of Exercise and the Brain by John Ratey, MD, but the central mechanism is brain-derived neurotrophic factor (BDNF). BDNF causes neuron growth and is triggered by exercise. It promotes positive effects, such as improving learning and mood, while protecting against negative effects, such as cell death. Studies have shown that more physically fit kids do better in school. In addition, starting the school day with exercise improves students’ performance more than studying for an extra hour.

Once the brain effects had me hooked, I kept learning more benefits of exercise.

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Research Design: Survey Design, Demographics, Validity, and Reliability

Surveys are prominent in quantitative educational research for measuring students’ opinions or self-reported data, such as study habits. Data collected from survey research can be any level of measurement. For example, if you ask the question “How much time did you study per week?” you could collect ordinal data (e.g., “none” “some,” or “a lot”) or ratio data (e.g., number of hours). This example also illustrates the most common criticism of survey data, which is that it can be unreliable. Even if students don’t skew their answers to be socially desirable based on what they think the researcher wants to hear, they might not be able to give an accurate response from memory. For validity, it is better to have direct measurements, but in education, this is often impractical or invasive. Evaluate the trade-offs in your work.

Interval data in surveys is commonly collected with Likert-type scales. The classic Likert (pronounced lick-ert) scale is a 5-point scale: 1- Strongly Disagree; 2 – Disagree; 3 – Neither agree nor disagree; 4 – Agree; 5 – Strongly Agree. Likert-type scales can range from 3 to 7 points, depending on how much sensitivity is desired. People tend to be less reliable when making more than 7 distinctions, so providing more choices can lead to unreliability. If you want to force people to choose an option other than neutral, provide an even number of choices to avoid a neutral option. The anchors/endpoints for Likert-type scales can be anything, but people are most familiar with “Strongly Disagree” to “Strongly Agree.”

It’s important to note that there is a debate about whether scale data is interval or ordinal, and there is a case for both. The ordinal side would argue that “4 – Agree” cannot be interpreted at 2 points higher than “2 – Disagree,” which makes a lot of sense. The interval side would argue that the scale represents a range of agreement with even intervals, making it most like interval data. I tend to treat scale data as interval if 1) the number of points (i.e., 3-7 points) is high enough to be more discerning than ordinal data and 2) the number of participants is high enough to overshadow the error that is inherent when asking people to pick an option on a scale.

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Research Design: Levels of Measurement

Operationalizing Quantitative Measurements

Your measurements are your dependent variables. In educational research, one of the dependent variables is frequently learning. To measure learning, we have to define exactly what we mean. Learning could be measured by grades on assignments, such as exams or projects, performance on standardized tests, such as concept inventories, or self-report, such as feelings of learning. All of these options are possible, though some are more defensible in scientific research (more on validity later). As a researcher, you need to operationalize, or clearly articulate in a way that can be applied to your research, what you mean when you say learning, or any of your other dependent variables. You might need to operationalize your independent variables, too. For example, instead of saying you’ll measure peer-to-peer interaction, you could operationalize these interactions as number of posts on a peer-to-peer forum and number of contributions during peer-to-peer discussions in class.

Levels of Measurement for Quantitative Data

Levels of measurement describe the type of quantitative data that you have by categorizing the relationships among values of a variable. When we represent information as numbers, such as representing learning as grades, we must be mindful of what these numbers represent. Unlike in math, higher numbers do not always mean higher value in quantiative data. For example, if you’re recording learners’ race, you might code “Black or African American” to be 1, “Hispanic or Latino” to be 2, etc. for purposes of analysis. This coding does not mean that Hispanic or Latino is more valuable than Black or African American, but it is merely a way of distinguishing between the two. On the other hand, if you’re measuring learners’ test scores, then a score of 80 would have a higher value than a score of 70. Levels of measurement categorize these relationships to determine which statistical tests are appropriate to analyze your data. There are four levels of measurement.

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Research Design: Qualitative, Quantitative, and Mixed Data

Your measurements, or dependent variables, provide the data you will analyze. There are two main types of data.

Quantitative data represent the world with numbers that can be statistically analyzed. They are necessary for relational or causal research questions because these types of questions require inferential statistics that generalize the results from your study to a larger population (more on this later). For example, quantitative data could tell us the average number of forum posts per student or the number of times students watched a video. Quantitative measures are appropriate when you want to confirm a hypothesis (e.g., that students in one group outperform others), but they are close-ended, which does not allow for exploration.

Qualitative data, in contrast, are open-ended and used mainly for descriptive research questions. For example, qualitative data could tell us what a student posts on a forum or notes a student took while watching a video. Qualitative measures are appropriate when you want to explore a phenomenon (e.g., how students use forums), especially if you want that exploration to be driven bottom-up by the learners rather than top-down by the researchers. However, qualitative data can be highly detailed and time-consuming to analyze. As a result, many researchers will analyze qualitative data quantitatively to summarize the data for future readers. Qualitative data can be quantified using coding schemes that turn descriptions into numbers. For example, for coding a forum in a physics class, you could use a coding scheme that counted the number of times students mentioned each of Newton’s Laws of Motion. Quantifying data allows qualitative data to be used in statistical analyses and for relational and causal questions.

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Research Design: Dependent and Independent Variables

Variables in education research are anything that can have different values or vary across learners. Dependent variables are the outcome variables that you collect data about in research, like learning outcomes. They apply to all research designs: non-experimental and experimental. All measurements used to evaluate or understand learning or a learning environment, such as test scores or attitudes, are dependent variables. Pre-tests and post-tests are dependent variables.

Independent variables represent differences in groups that you think might impact the dependent variables. Independent variables can be fixed, meaning they are manipulated by the researcher, or random, meaning they are pre-determined. Fixed independent variables (e.g., instructional style) are used in experimental designs, and participants must be able to be assigned to one value of the fixed variable (e.g., class instruction is based on lecture or active learning). The researcher manipulates the fixed variable to explore its effect on the dependent variable(s). Random independent variables (e.g., gender or religion) are used in non-experimental designs. These variables are not manipulated, but they can still represent a difference between groups on dependent variable(s). Random variables also include manipulable variables that are not manipulated, like which section of a course a student is in. If you have both fixed and random variables, then you have a quasi-experimental design.

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Research Design: Non-Experimental and Experimental Designs

The type of research design that you need depends on the type of research question that you have. Descriptive and relational questions can be answered with non-experimental designs, and causal questions must be answered by experimental designs. Note: these design categories are independent from pre-test and post-test designs, so you can have a pre-post non-experimental design or a pre-post experimental design.

Non-Experimental Design (descriptive and relational questions)

In non-experimental designs, researchers are measuring phenomena as they exist in the world, and they are not systematically manipulating anything, meaning there is no intervention. Because no systematic manipulation occurs, these designs can answer only descriptive or relational questions. Interactions between researchers and the participants in the study should be limited to what is necessary for collecting data. To collect data, researchers might ask participants to fill out surveys or another type of measure. If direct interaction with participants is impossible or might invalidate the data by biasing participants, an observational approach might be appropriate. In observational research, researchers do not directly interact with participants, but they collect data by carefully observing participant behaviors. An example of observational research would be counting the number of contributions from each student in an in-class discussion.

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Research Design: Pre- and Post-Tests

When you collect data has important implications for the conclusions that you can draw from that data. In education research, we often try to measure a difference, such as what students learn or how their experiences or perceptions change. Because we often try to make conclusions about differences, it can be equally important to take measurements at the beginning and end of a study.

Pre-Post Design

To measure a difference, we need to measure the level at which students start, such as their prior knowledge, and the level at which students finish, such as after a course or intervention, to make claims about how they’ve changed. The type of design that measures before (pre-test) and after (post-test) an intervention is called a pre-post design. This design is good at measuring any change from before the research started to after, such as how students’ perceptions of computer science differ from the beginning to the end of a course.

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Research Design: Research Questions

Many research questions in education come from observing something unexpected in the classroom, reading about a new method of instruction, or learning about a new tool. For example, you might find that a student has a unique explanation for a concept and want to know if it would help other students. Or you might have read about a metacognitive strategy and want to know if using that strategy would improve learning outcomes in your class. Your research methods will depend heavily on what type of research question you have.

There are three main types of research questions: descriptive, relational, and causal. A good research question in education explicitly identifies

  • the group that you are studying, such as online students, and
  • the variables you intend to manipulate or measure, such as the instructional strategy or student experience.
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Research Design: Series Introduction

Education research aims to understand how people learn and the effects of learning environments, including sociocultural factors. The ultimate goal is typically to improve learning and enable learners to achieve their goals. How researchers build this understanding and achieve this goal is called research design, which is critical to the quality and validity of the knowledge produced. Research design includes several aspects:

  • Crafting research questions that are interesting and answerable
  • Selecting research methods that are appropriate and thorough
  • Identifying or designing measurements that provide reliable and valid data
  • Conducting appropriate analysis of data based on the type of data and the research questions

Because my Ph.D. is in a social science, psychology, about half of my graduate coursework was about research design. In the computer science education community, the most requested talk in my repertoire is about research design. This interest is likely due to many people in our field not having formal training in this critical aspect of education research. Instead, they learn these skills primarily through apprenticeship. This series is designed to help those learning research design through an apprenticeship model.

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Wellness: Recovery: Dopamine – Recognizing Addiction and Good Hygiene

Many people have heard that dopamine is responsible for feelings of reward, but that’s only part of a highly complex system. The dopamine system developed to motivate us to seek things that have a cost or risk to attain. Evolutionarily, this included hunting for food, getting water, or gathering information. Dopamine motivates us to do things that we wouldn’t do for fun but are essential to survival. Because it works to motivate us, it is responsible for feelings of agitation (e.g., craving) as well as reward.

Our dopamine system maintains a balance over time. When we are at a healthy base level of dopamine, we feel an agitation that makes us motivated and focused on pursuing goals. When we achieve a goal, we have a surge of dopamine. The bigger the achievement, the bigger the surge. But there is a downside. Whenever dopamine surges above baseline, it must balance by dropping below baseline. The bigger the surge, the bigger the drop. This dopamine balance is why people sometimes feel unmotivated and lost after achieving a big goal. Low levels of dopamine inhibit our ability to feel motivated and focused.

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