Teaching and Learning blog

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, 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.

Located in the heart of downtown Indianapolis, Indiana University – Purdue University Indianapolis (IUPUI) is a vibrant higher learning institution, enrolling a diverse student body that includes more than 16,000 undergraduates.

At IUPUI, Math faculty have long trusted and adopted MyLab Math from Pearson, a dynamic platform that’s driving improved performance and high satisfaction for students and faculty alike. It’s described as an integral part of the math curriculum, purpose-built to effectively adapt to individual students and their unique learning needs.

The adoption and success of MyLab Math at IUPUI goes hand in hand with Pearson’s commitment to going above and beyond as a dedicated partner every step of the way.

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, 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.

Amy Pope is an award-winning senior lecturer in physics and astronomy at Clemson University. A Clemson alumna herself, Amy has her bachelor’s, master’s, and doctoral degrees in physics, and has devoted the last 22 years to teaching physics at her alma mater.

Clemson has “a large focus on teaching and making sure that students have the number one engagement experience in their classes,” Amy explains. Which is part of what makes Clemson stand out, in addition to being a “fun, close-knit community.”

Amy shares Clemson’s commitment to delivering engaging learning experiences, while also making learning affordable. As Amy describes it, “excessive cost is certainly a barrier to student success.” With that in mind, it’s a priority for Amy to use an affordable, effective learning platform, tied to a physics textbook she can trust.

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.

John Cummings is a senior lecturer in the Department of Biological Science at Clemson University. He’s an award-winning educator who has furthered the use of innovative technology at Clemson. He also gets shout-outs from Clemson students on TigerNet message boards. According to one commentor, “John Cummings for Anatomy and Physiology is pretty awesome.”

John currently teaches Human Anatomy and Physiology I and II, and a lot of the nursing students in his classes go on to take the MCAT. Many of those students report back to John that the work they did in his A&P class had a positive impact on their MCAT scores.

“I know from unsolicited note cards and emails that I’m getting from students,” John explains, “that their highest portion on the MCAT was the bio because of what happened in Anatomy and that they’re feeling really positive. And that’s because of the foundation that’s there. What we’re teaching them to do is be independent learners.”

Improving student performance

John was an early adopter of Pearson’s Mastering® A&P digital learning platform and has a vivid memory of the first time his Pearson sales rep did a Mastering demo for him. “I was on board immediately,” he recalls, “because it saved me from having to do a lot of the stuff that I had been doing in putting together my own things, [with the] automatic grading and all of that sort of stuff. It’s one of those things that can really sell itself.”

From that first demo, John says that Mastering appealed to him “because it’s conveniently accessible.” He felt it would add elements to his courses, benefiting him and his students. “If it doesn’t do something for my students,” he adds, “I'm not going to maintain it in my class.”

John kept a critical eye on progress as he implemented Mastering into his courses at Clemson, and the impact was obvious from the first semester. “There was a tremendous improvement in student performance that first year,” he explains.

Instructors are able to see how much time a student spends on a question in Mastering, an insight that John feels gives him a sense of which students are truly answering a question versus letting a quick Google search find the answer for them. “Initially, when I first adopted it,” he recalls, “the people who significantly worked through the Mastering assignments saw about a seven percent increase in performance on examinations.”

In recent years, researchers have increasingly advocated for open science (some scholars capitalize it as Open Science). The open science movement consists of several practices that make research reports more transparent, such that details regarding methodologies, collected data, and data analysis procedures are fully disclosed and open to public inspection and critique.

Here are five common ways in which researchers can enhance the transparency — and ultimately also the credibility — of their research projects. These points are described in greater depth in my book Practical Research: Design and Process, 13th Edition:

• Preregistration of a study: Researchers post an online description of what they will do in an upcoming research project. Among other things, such a description usually includes the overarching research question(s) to be addressed, specific hypotheses to be tested, specific methods to be used to collect data, and specific statistical procedures and other strategies to be used in analyzing the data. (See Chapter 5.)
• Results-blind reviewing: Before a project is conducted, outside reviewers examine a preregistered research proposal to determine the apparent soundness of a researcher’s main question(s), rationale, a priori hypotheses, study design, and planned data-collection and data-analysis strategies. (See Chapter 13.)
• Registered report: When a proposed project has been both (a) preregistered and (b) results-blind reviewed, an editor of an academic journal commits to publishing the final research report in that journal — regardless of whether the results are statistically significant or in some other way especially noteworthy — as long as the project has actually been carried out as originally proposed or else modified in reasonable, well-documented ways. (Again, see Chapter 13.)
• Open access to specific data collection and analysis procedures: Researchers make the details of their data-collection and data-analysis strategies readily available to interested scholars; for example, they might share the questionnaires administered, specific statistical procedures conducted, or particular coding schemes used to find patterns in participants’ interview responses. (Again, see Chapter 13.)
• Open access to raw data: Especially when qualitative data has been collected (e.g. when people have been interviewed or members of a particular sociocultural group have been carefully observed as they’ve gone about their daily activities), the actual verbal and/or nonverbal behaviors might be presented in an appendix or supplemental materials. There’s an important caveat here: A researcher can give other people open access to their raw data only when either (a) responses remain strictly anonymous or (b) participants have given explicit written permission for their identities to be disclosed. (Once again see Chapter 13; also, see the section “Ensuring Participants’ Rights and Well-Being” in Chapter 4.)

When striving for transparency, researchers don’t necessarily do all of these things for a particular research project. For example, when a researcher is building on another researcher’s work and uses a questionnaire that the other researcher has created, publishing the questionnaire might violate the other researcher’s intellectual property rights. And preregistration of a project isn’t always logistically feasible. For instance, many action research projects may be intentionally ill-defined at the very beginning, at least in part because initial data collection may lead to new questions to be addressed and further data collection may be necessary.

The bottom line here is this: I recommend open science practices when they can enhance the credibility of a research project without jeopardizing either (a) the privacy and well-being of the people being studied in the project or (b) the degree to which the project can thoroughly address the questions that underlie and drive the project.

In much of the 20th century, most researchers published results and conclusions with the hopes that other individuals would translate their findings into effective, “real-world” practices and interventions. In recent decades, however, many researchers have wanted applications of their findings to be integral parts of their own research projects. In particular, the term action research refers to projects that are designed not only (a) to make sense of an issue or problem but also (b) to take action and make concrete changes in conditions, practices, resources, and/or policies. In a subgroup of action research designs known as participatory designs, some researchers include one or more practitioners or other key stakeholders — people who can make use of a study’s findings in their future decision-making and intervention efforts — on their research teams.

Action research is almost invariably eclectic in its use of specific data collection strategies. For example, research projects might incorporate a combination of such strategies as surveys (face-to-face, paper-pencil, and/or online), individual and small-group interviews (e.g. focus groups), in-depth case studies, observations of people’s behaviors in real-life environments, and comparisons of groups in distinctly different educational or therapeutic settings.

Many action research projects are iterative in nature; that is, researchers move back and forth among various steps in the research process. For example, when researchers begin to analyze their data — or even before that, when they are still collecting data — they may find that the issue they have been addressing is more complex and multifaceted than they initially realized, and so they may go back and reformulate the questions they need to address, the data they need to gather, and the means by which they can gather that data.

Another important way in which action research tends to differ from more traditional research methodologies is in researchers’ dissemination strategies — that is, the ways in which researchers try to get the word out about their findings and potential implications. Traditional researchers typically describe their studies and findings in journal articles, books or book chapters, conference presentations, and — for graduate students — master’s theses and doctoral dissertations. Such reports can be quite effective in communicating findings to individuals working in the same or a similar field at colleges, universities, and other research settings, but the great majority of them escape the attention of practitioners, policy makers, and the public at large. Hence, action researchers often use additional, more local, strategies to broaden the audience that has access to their findings. Examples are websites, webinars, chatrooms, blogs, newsletters, in-person community forums and panel discussions, and popular social media platforms (e.g. Facebook, TikTok).

As we human beings learn new things every day, we all have ideas about what “knowledge” and “learning” are—ideas that are collectively known as epistemic beliefs. These beliefs typically include beliefs about many or all of the following:

• The certainty of knowledge: Whether knowledge is a fixed, unchanging, absolute “truth” or, instead, a tentative, dynamic entity that will continue to evolve over time.
• The simplicity and structure of knowledge: Whether knowledge is a collection of discrete, independent facts or, instead, a set of complex and interrelated ideas.
• The source of knowledge: Whether knowledge comes from outside of learners (i.e., from a teacher or other authority figure) or, instead, is derived and constructed by learners themselves.
• The criteria for determining truth: Whether an idea is accepted as true when it’s communicated by an expert or, instead, when it’s logically evaluated based on available evidence.
• The speed of learning: Whether knowledge is acquired quickly, if at all (in which case learners either know something or they don’t, in an all-or-none fashion) or, instead, is acquired gradually over a period of time (in which case learners can partially know something).
• The nature of learning ability: Whether people’s ability to learn is fixed at birth (i.e., inherited) or, instead, can improve over time with practice and use of better strategies.

Keep in mind that epistemic beliefs aren’t as either–or as I’ve just portrayed them. Most or all of the dimensions I’ve listed are probably continuums rather than strict either–or dichotomies.

Psychologists often use certain terms when referring to various beliefs about the nature of knowledge. Typical of 3-year-olds is a realist view, in which knowledge is the same as what people say or do (e.g., if I tell you that some cows have purple fur with orange spots, you’ll take my word for it). Four-year-olds are more likely to have an absolutist view, in which knowledge isn’t necessarily the same as people’s thoughts or assertions but it’s certain and definite—things are either absolutely right or absolutely wrong. Later on—typically in adolescence at the earliest—some individuals acquire a multiplist view, in which some knowledge is seen as uncertain, with people’s varying opinions all having equal legitimacy. People may or may not eventually acquire an evaluativist view, in which people’s ideas and opinions have more or less merit and legitimacy depending on whether defensible evidence or logic supports them.¹

It makes sense to hold an absolutist view about some kinds of knowledge. Certain bits of information are fairly black and white; we usually think of them as “facts.” For example, France is a country in Europe, Christopher Columbus first sailed across the Atlantic in 1492, and two things plus two more things give us four things altogether; these facts are unlikely to change in the foreseeable future. In other situations, a multiplist view makes sense. For example, there isn’t necessarily a single “right” answer to questions such as “What qualities are essential for ‘good’ music?” and “Is it appropriate to burp when you’re a dinner guest in someone else’s home?”

When conducting research on complex issues or problems, however, good researchers adapt an evaluatist perspective: They recognize that a particular premise or conclusion is probably “true” only to the extent that concrete evidence and logic support it. Accordingly, taking an evaluatist view requires researchers to engage in at least three mental processes:

• Critical thinking. Good researchers never take the things they read or hear at face value. Critical thinking involves evaluating the accuracy, credibility, and worth of information and lines of reasoning. For example, when people read about other individuals’ theories and research findings, they regularly ask themselves such questions as these: “Are there potential shortcomings in this research study that make me question the validity of the researcher’s conclusions?” “Does this researcher’s explanation make sense based on other research findings related to the issue being investigated?” “How might I improve on the research methods used in this study?”
• Metacognitive reflectiveness. The term metacognition means “thinking about the nature of thinking,” and metacognitive reflectiveness means “thinking about one’s own thinking.” Good researchers regularly reflect on their own thought processes, mentally checking themselves regarding their own logic. For example, they continually ask themselves whether they’re being as objective as possible in their observations, whether their evidence adequately supports their hypotheses and conclusions, and where there might be holes or inconsistencies in the theories they have constructed to explain a phenomenon they are investigating. Metacognitive reflectiveness, then, requires considerable critical thinking.
• Conceptual change when warranted. Conceptual change involves significantly revising one’s existing beliefs about a topic, enabling new, discrepant information to be better understood and explained. Good researchers regularly revise their beliefs, understandings, and explanations as credible new evidence and theories appear on the scene. In general, they keep open minds about the true nature of the phenomena they are investigating. Researchers who do otherwise—those who stubbornly stick to their own previous explanations even in the face of considerable contradictory information—impede scientific progress as we collectively strive to better understand our physical, psychological, and social worlds.

1 For groundbreaking work on this developmental trend, I refer you to two book chapters by Deanna Kuhn and colleagues:

• Kuhn, D., & Franklin, S. (2006). The second decade: What develops (and how)? In W. Damon & R. M. Lerner (Series Eds.), D. Kuhn & R. Siegler (Vol. Eds.), Handbook of child psychology: Vol. 2. Cognition, perception, and language (6th ed., pp. 953–993). New York, NY: Wiley.

• Kuhn, D., & Weinstock, M. (2002). What is epistemological thinking and why does it matter? In B. K. Hofer & P. R. Pintrich (Eds.), Personal epistemology: The psychology of beliefs about knowledge and knowing (pp. 121–144). Mahwah, NJ: Erlbaum