Teaching and Learning blog

Even in graduate school, many students have misunderstandings about the nature of both science and scientific research. In this brief blog post, I hope to correct certain misconceptions my readers might have about these two concepts.

What does true scientific research entail?

In Chapter 1 of the book, Practical Research: Design and Process, 13th Edition, I identify activities that do not constitute true research as scientists use the term, and they’re worth repeating here. In particular, scientific research is not simply:

• Gathering information that already exists but is currently unknown to an individual making an inquiry about a topic.
• Rummaging around for existing information that is hard to find but is important for decision-making regarding an important project or problem.
• Transporting information from one location to another — for example, by writing a “research report” that a student submits to meet a course requirement.

Instead, true scientific research is a process of systematically collecting, analyzing, and interpreting data to enhance understanding of a particular phenomenon. Let’s look at specific words within this definition:

• Scientific research is systematic — that is, it is conducted in a somewhat preplanned, organized fashion, rather than being a totally rudderless, willy-nilly endeavor. As you will see, exactly how systematic a research endeavor is depends on the research methodology being used; for example, on average, quantitative research is more preplanned and systematic than qualitative research.
• Scientific research involves collecting new data — perhaps measurements of a certain physical phenomenon, people’s responses to a certain survey, or observations of commonplace behaviors within a certain social or cultural group. (Please note an exception here: Consistent with open science practices, a researcher might make use of another researcher’s data in a quest for additional insights that those data may yield.)
• Scientific research also involves analyzing a body of data to find patterns within it — patterns that might shed light on a phenomenon that has previously been poorly understood.
• Finally, scientific research involves interpreting and trying to make sense of those data — going beyond the data themselves to draw conclusions about the underlying phenomenon being studied.

Driving the whole research endeavor are one or more research problems or questions that the researcher is trying to address and potentially solve. In Chapter 1 I describe such problems and questions, and in Chapter 2 I give you considerable guidance about how to identify appropriate ones for your own research projects.

What, exactly, is science?

When children first hear the term “science” in elementary and secondary school grades, their teachers are typically referring to biology, chemistry, physics, astronomy, and the like. But in fact, most academic disciplines — not only the biological and physical sciences, but also the psychological and social sciences — have elements of science at their core.

The helping professions, too, are rooted in science. Medicine and health care are rooted in biology, but so are education, psychotherapy, social work, coaching, and many other helping professions dependent on scientific findings.

Here I simply want to highlight three things that a scientific approach to a problem or question typically involves, plus one thing that it definitely does not involve:

• Hypothesis formation and testing: When conducting a research project, many scientists begin with one or more tentative hypotheses regarding processes or dynamics that underlie the phenomenon under investigation; they will then design a study that can enable them to systematically test those hypotheses. In some cases, however, researchers might decide not to form any hypotheses at the outset, typically in an effort to be as open-minded as possible about what the collected data might reveal. Nevertheless, in virtually all scientific research, one or more hypotheses eventually emerge as potential explanations for the phenomenon at hand, and at some point during a project, those hypotheses are tested.
• Construction of theories and/or models: Scientific inquiry is a very constructive process. Often it involves constructing or revising a theory—an integrated set of concepts and principles developed to explain a particular phenomenon. Alternatively, it might involve constructing a model—a physical or graphical representation that shows how certain entities might be interrelated parts of a larger system. As examples, you’ve undoubtedly seen physical models of the sun and various planets in our solar system, and you’ve seen graphic models of various phenomena (perhaps including cause-and-effect relationships) in college textbooks.
• Some degree of objectivity: Researchers strive to be as objective as they can in their quest to collect, analyze, and interpret data. Unfortunately, thanks to the various expectations, biases, mindsets, and worldviews that researchers inevitably bring to any research project, complete objectivity is virtually impossible. As you read the book, you’ll discover the many ways in which the human mind can distort the true “realities” of our physical, psychological, and social worlds — if such realities exist at all. You’ll also discover that, especially in the psychological and social sciences, many researchers have acknowledged that they can never be completely objective in their research findings and conclusions.
• Recognition that it is virtually impossible to prove anything beyond a shadow of a doubt: As our international scientific community increasingly adds new information to our knowledge base about various physical, psychological, and social phenomena, we can continue to zero in on things that might be true about the world and the larger universe in which we live. But for logistical and statistical reasons, we will probably never know what things are actually true. For example, as a high school student back in the 1960s, I was taught that atoms and the protons, neutrons, and electrons within them were the smallest components of the physical universe. But then along came quarks, muons, neutrinos, and other entities that are seemingly even smaller.

I should mention here that the impossibility of proving something doesn’t mean that the theories and models that scientists construct to explain their findings have no merit. As more and more data related to a particular phenomenon is collected, some theories and models are regularly revised to take that data into account; meanwhile, other theories and models might be discarded because they cannot adequately account for the new data.

Even though a given theory cannot ultimately be proven to be a complete explanation of a phenomenon, it can nevertheless have considerable usefulness in making predictions and identifying interventions that can enhance the well-being of human beings, other living things, and the planet on which we all live.

Researchers’ particular beliefs about the nature of human knowledge can have a major impact on the quality of the research they conduct, the conclusions they draw, and, ultimately, the impact they have on our communal knowledge of the world as a whole. I expand on this point in a separate blog post: “How researchers’ epistemic beliefs influence the quality of their work.”

Researchers’ beliefs about legitimate and credible research designs, methodologies, processes, and practices have expanded in a great many ways since the 1970s, and I — who obtained my bachelor’s, master’s, and doctoral degrees in the 1970s — have observed the continual expansion over the past several decades with both awe and delight. There is so much more that we, as a larger society, can learn when we look at puzzling phenomena from multiple angles and perspectives!

Noteworthy Changes in Practical Research, 13th Edition (2024)

To capture recent trends in research methodologies and perspectives, I have made several noteworthy changes to the content of the latest edition:

• I have significantly reorganized and greatly expanded discussions of various action research and participatory designs (see Chapter 10). A separate blog post presents a brief description of action research.
• I have increased discussions of the ethics of research and possible biases that might adversely affect the quality of a research project and/or research report (see Chapters 4, 6, 7, 8, 9, 10, 11, 12, and 13).
• I have added discussions of open science practices (see Chapters 5 and 13). A separate blog post provides a short overview of what open science can entail.
• I have included several new illustrative examples of research methodologies (see Chapters 8, 9, and 10).
• With the assumption that my readers are now more technologically literate than they were even a few years ago, I have updated discussions of technology-based strategies (e.g. new software options) while cutting back on coverage of more elementary strategies (e.g. how to use word-processing software).
• I have added five new Conceptual Analysis features with which readers can self-assess their understanding of key concepts and principles in the book (see Chapters 1, 6, 11, and 12).
• At the end of each chapter, I have added a short summary that can help readers mentally revisit and review central ideas within the chapter.

Beginning with the 12th edition, the book no longer includes a chapter on historical research. (With significant advances in other research methodologies, there was simply no longer any room in the book for it.) Yet historical research methodologies are essential for students in history and related disciplines and can also be useful in other disciplines. Readers who would like to learn more about historical research can find an entire chapter on the topic in the book’s 11th edition, published in 2016.

Two changes in the latest, 13th edition of Practical Research appear on the book’s cover and title page. First, the subtitle of the book, previously Planning and Design, has changed to Design and Process. Although this change might strike my readers as a subtle one, it reflects the ways in which the book’s contents have evolved over the years to focus increasingly on the many, many simultaneous and sequential processes — not only physical processes but mental processes as well — that underlie well-designed, high-quality research endeavors.

Second, whereas Paul Leedy and I have been coauthors of the past several editions of the book, I am now listed as sole author. To explain this change, I must go back to the very first edition. In the early 1970s, as a professor at American University, Paul saw the need for a research methods book that provided specific, concrete guidance for students and other novice researchers who were working on independent research projects (e.g. projects for masters’ theses and doctoral dissertations). The result was the first edition of the book, published in 1974.

As various research methodologies continued to evolve in many academic disciplines, Paul updated the book in 2nd, 3rd, 4th, 5th, and 6th editions (with contributions by Tim Newby, Peggy Ertmer, and the editorial staff in the 6th edition).

With Paul’s eventual retirement, his editor, Kevin Davis, asked me to take over, beginning with the 7th edition. Paul’s ideas and words have always been key elements of the book, however, and thus it was quite appropriate to list us as coauthors. But as I wrote the 13th edition, the book had changed so much that it made sense to Pearson’s editorial staff that I become sole author.

Mental Health Awareness Month began in 1949 – established by Mental Health America – as a way to increase awareness and reduce stigmas associated with mental health issues, as well as promote the vital role that mental health plays in overall health. As a leader in global education, Pearson recognizes our unique responsibility to support and raise awareness for important issues like this one.

Mental Health vs. Mental Disorder

The CDC defines mental health as “our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make healthy choices.”¹ Everyone can take action to improve their own mental health.

Mental illness or mental disorder is a diagnosable condition. A mental health professional can help to diagnosis a disorder and may look at things including symptoms, severity, and length of time to make the diagnosis.

The Numbers Tell the Story

While exact numbers may be challenging to find, it is estimated that millions of Americans are living with a mental health issue. According to the National Alliance on Mental Illness (NAMI), within the United States 1 in 5 adults encounter a mental health issue each year with 1 in 20 experiencing a serious issue each year. 1 in 6 within the ages of 6-17 experience a mental health issue each year.² And a recent survey done by the National Center for Health Statistics (NCHS) shows that mental health treatment has increased over the last three years. ³ The map below highlights impact across the United States. Chances are you know someone that may be affected by or struggling with a mental health issue. So, for yourself or for someone in your life, educating yourself on mental health issues is important.

Using technology to improve teaching and learning is in Pearson’s DNA. As the first major higher education publisher to integrate generative AI study tools into its proprietary academic content, Pearson is excited to be harnessing the power of AI to drive transformative outcomes for learners. We are focused on creating tools that combine the power of AI with trusted Pearson content to provide students with a simplified study experience that delivers on-demand and personalized support whenever and wherever they need it.

In this multi-part blog series, you’ll have a chance to hear about AI innovations from Pearson team members, faculty, and students who have been involved with the development and rollout of Pearson’s AI-powered study tools.

“It’s the best textbook I’ve ever read because there is no fluff and it’s easy to read,” says an economics student at the University of North Carolina at Greensboro (UNCG). “You actually want to read it.”  

A textbook college students want to read?   

Unusual though it may seem, many economics students at UNCG express comments like these, ever since their professor began using Revel: Microeconomics Interactive, 1st Edition by Parkin, Bade, and Sarbaum.  

What course(s) do/did you teach?

Greg: General Biology – Majors; General Biology – Nonmajors; Genetics; Developmental Biology; Microbiology

What is a challenge that you’re currently facing in the classroom? How did/do you try to overcome this challenge?

Greg: Helping students who struggle to understand biology. Additionally, increasing course structure.

What is the biggest lesson you’ve learned in the past few years regarding teaching biology?

Greg: The importance of focusing on clearly articulated learning objectives.

What is one best practice that you use that you think works well and you would want to share with others, whether it's in a classroom setting, working in groups, or working one-on-one with a new teaching technology?

Greg: Creating a course structure that encourages understanding biology for most students.

What are you most proud of in your career?

Greg: Hearing from students who have gone on to careers in biology, medicine, and related fields who have told me of the importance of courses I’ve taught.

In your opinion, what is higher education going to look like in the next two to three years?

Greg: Generative AI is likely to be transformative in positive and negative ways that are difficult to predict precisely.

The 8th Edition of Biological Science is being released this year. What excites you the most about this revision?

Greg: The suite of new features, particularly “Biology in Numbers,” coupled with the solid core of a text that illustrates what we know about biology and how that knowledge was gained.

Using technology to improve teaching and learning is in Pearson’s DNA. As the first major higher education publisher to integrate generative AI study tools into its proprietary academic content, Pearson is excited to be harnessing the power of AI to drive transformative outcomes for learners. We are focused on creating tools that combine the power of AI with trusted Pearson content to provide students with a simplified study experience that delivers on-demand and personalized support whenever and wherever they need it.

In this multi-part blog series, you’ll have a chance to hear about AI innovations from Pearson team members, faculty, and students who have been involved with the development and rollout of Pearson’s AI-powered study tools.

What course(s) do/did you teach and where?

Kim: I designed, coordinate, and teach Biology 202: Introduction to Biology: Evolution and Ecology at Salisbury University in Maryland.

What is a challenge that you’re currently facing in the classroom? How did/do you try to overcome this challenge?

Kim: Some students are thriving in college but others are struggling in diverse ways: mental health challenges such as depression, social anxiety, and climate anxiety; social injustice; financial insecurity and food insecurity; working long hours at jobs; navigating college as first-generation students and transfer students; neurodiversity challenges, and so on, some experiencing a high level of intersectionality of marginalized identities.

To address these challenges I employ many evidence-based inclusive practices in the structure and culture of my course to promote a sense of empathy and community. I try to center diversity (in its many dimensions) and equity in our educational mission to help students to feel a sense of belonging, support, agency, and clarity-of-mission in our learning space. I also try to get to know the students well enough (fortunate with small class sizes) to help connect them to appropriate supports.

What is the biggest lesson you’ve learned in the past few years regarding teaching biology?

Kim: The affective domain (feelings, attitudes, emotions) is so important to student success, equity, and retention in STEM.

In my classroom and in Biological Science, we weave together attention to the affective, metacognitive, and cognitive domains. For example:

• The Insider Tip Videos of peer learners and Making Models exercises and videos provide tips on tough science concepts and skills while encouraging growth mindset, value, interest, and self-efficacy.
• Formative and summative assessment questions applying concepts and skills to societal challenges and solutions, including End-of-Chapter Case Studies and Human Angle questions (with photos showing diverse scientists at work in career contexts) promote interest, value, science identity, and self-efficacy.
• Reflect questions and supporting BioSkills promote value and self-efficacy in practicing metacognitive skills.
• Biology in Numbers problems and videos promote interest in math and growth mindset.

In essence, it helps to support the students holistically, as thinking, feeling humans.

What is one best practice that you use that you think works well and you would want to share with others, whether it's in a classroom setting, working in groups, or working one-on-one with a new teaching technology?

Kim: Since teaching and learning requires a systems-thinking approach, it is difficult to mention just one best practice without connecting it to others in synergy.

One structural best practice that I recommend is a transparent and genuine focus on learning outcomes (focusing on both concepts and skills), transparent alignment of assessment to each outcome, and transparent alignment of homework and classwork to the outcomes.

This inclusive approach keeps instructors and classwork on task, removes guesswork from the course experience for students, and thus helps students genuinely focus on their learning, especially when multiple attempts and demonstrating achievement of outcomes are built into the learning system.

What are you most proud of in your career? 

Kim: In terms of my classroom teaching, in the last four years I have had a leadership opportunity to rebuild the introductory biology curriculum for majors at Salisbury University from the ground up. This has been a career capstone opportunity/challenge where I could synthesize 20 years of personal experience and best practices from the science education and social justice communities.

My team employed a backwards designed, flipped course organization with high structure. We centered the curriculum on:

• The Vision and Change (AAAS, 2011) core concepts and competencies,
• Standards-based grading with transparent and centered learning outcomes and multiple attempts to demonstrate mastery of learning outcomes on case-based exams focused on health and environmental sustainability,
• Team-based active learning,
• A course-based undergraduate research experience (SUPP),
• Inclusion of counter-stereotypical scientist role models and science-allied career options,
• Metacognition, value-affirmation, and growth mindset training,
• A biophilic method of supporting engagement, mental health, sustainability, and social justice,
• And a number of built-in methods of collecting evidence of efficacy.

While we continue to use evidence to improve the courses every semester, the transformation has been invigorating because students are engaged in an active community of learning.

In your opinion, what is higher education going to look like in the next two to three years?

Kim: According to the Journal of Higher Education, the undergraduate study body will continue to diversify over the next decade. This diversity is good for science, but in order to retain diverse students in our science programs we must collectively pivot to more inclusive practices, especially in our larger “gateway” courses for STEM majors where opportunity gaps tend to be deeper.

Fortunately, there is abundant evidence of numerous effective inclusive practices that help not only historically marginalized students but others as well. The main challenge is effecting broad and rapid institutional transformation on a national level.

The 8th edition of Biological Science is being released this year. What excites you the most about this revision?

Kim: At this time of climate crisis, biodiversity crisis, social justice reckoning, and other social challenges, it is more appropriate than ever to help students connect their biology learning to societal solutions, to envision themselves as potential scientists, and to see a link between their biology learning and solutions in their communities and society at large. Thus, it was a joy in this edition to encourage inclusion, value, and self-efficacy.  

For example, we updated the language and examples throughout the book to be more inclusive, narrowing the gap between the historical culture of Western science (heavily European/white/male) and the current culture of scientists and science students. The new Human Angle feature shows diverse scientist at work in a variety of contexts to help students imagine themselves in biology careers; the Insider Tip videos provide a relatable peer perspective and tips to help conquer challenging learning tasks; and revisions to text and questions help students see how their learning applies to solving current societal challenges. 

Using technology to improve teaching and learning is in Pearson’s DNA. As the first major higher education publisher to integrate generative AI study tools into its proprietary academic content, Pearson is excited to be harnessing the power of AI to drive transformative outcomes for learners. We are focused on creating tools that combine the power of AI with trusted Pearson content to provide students with a simplified study experience that delivers on-demand and personalized support whenever and wherever they need it.

In this multi-part blog series, you’ll have a chance to hear about AI innovations from Pearson team members, faculty, and students who have been involved in the development and rollout of Pearson’s AI-powered study tools.