Program

This proposal details an innovative semester-long, non-credit workshop that serves as a pathway for students to learn data analyses using R programming. The curriculum is built on a "Teaching Toolkit" of free and open-source resources, including DataCamp, Kaggle, and Google Colab with real-world local and national datasets (e.g., US Census API, World Value Survey). The pedagogical innovation lies in integrating various free tools and generative AI into the learning process. The workshop's effectiveness is demonstrated by student success, including two students receiving awards for presenting their data analysis at a regional conference.

This Teaching Toolkit assignment engages introductory statistics students in preregistration as an open science practice while developing foundational statistical competence. Prior to analyzing data, students preregistered their selected variables, hypotheses, and regression analysis plan using a publicly available dataset on Emerging Adulthood. This structured commitment precedes their interactive final poster presentations, where results are shared with peers. The activity integrates a motivational virtue ethics lens, encouraging students to reflect on the role of intellectual humility and courage in committing to analytic decisions before outcomes are known. It also encourages students to begin their final projects earlier. By linking technical training with research transparency, the assignment provides students with a model for understanding responsible research practices.

Many educational materials for psychological statistics courses can be costly and static. At Kennesaw State University, the undergraduate statistics course has historically had high rates of low grades, failures, and withdrawals. To address this, we integrated an existing OER into the LibreText platform, which supports embedding videos and interactive questions. We also developed corresponding online assignments on MyOpenMath, with a question bank that uses randomly generated datasets for repeated practice. Videos and automated feedback are embedded within assignments. We implemented these materials in four sections of the Statistical Applications course in Fall 2025. While data collection is ongoing, early evidence shows promising results and savings of about $75 per student. We will further discuss the impact on student learning outcomes and course performance.

Recent research emphasizes the importance of fostering purpose-driven engagement in STEM (Diekman et al., 2024; Thoman et al., 2025). This study examined how purpose reflection and its implementation format related to students’ psychological experience in a psychology statistics class. In the passive format, students heard about the instructor’s purpose. In the active format, students individually reflected on their own “why” for pursuing STEM. In the interactive format, students linked personal values to course content through both individual reflection and collaborative discussion. Compared to students who did not reflect on purpose (n = 97), those who did (n = 38) reported higher authentic belonging and enjoyment, with the most positive outcomes following the interactive format. Findings highlight purpose reflection as a possible effective evidence-based teaching practice. 

With inclusive datasets, students can learn from diverse perspectives while building their quantitative skills. This presentation shares a curated collection of classroom-ready resources built from open-access, identity-affirming research. The selected studies highlight the strengths and assets of identities that are often stigmatized. Specifically, they feature topics such as activism, well-being, and empowerment among participants who are transgender, disabled, bilingual, or refugees. Comprehension quizzes, simplified datasets, and analysis activities are provided for each article. The session will offer practical strategies for integrating these materials into statistics and methods courses. Together, these resources equip educators to incorporate more inclusive data in their courses, enhancing students’ understanding of diverse identities in research. 

In conjunction with our research methods sequence, we require students to develop, conduct, and analyze their own research projects across two semesters. In this presentation, we will explore the role of class example studies for student learning through purposefully curated shared class activities featuring experimentation, study design and critique, and survey creation. The results of these example studies are also analyzed together and provide low-stakes practice creating and presenting an academic poster before students present their own work.  Scaffolding these activities in a shared environment may increase depth of both engagement and mastery. 

Psychologists who work in clinical/professional settings are required to make evidence-based decisions daily. In addition to their own expertise and their client’s characteristics, these decisions should be based on the best available research. Much of the research in psychology, however, uses statistical methods that are not covered in most introductory statistics courses. This presentation discusses the creation of a second course in statistics that is designed to fill this gap by presenting more advanced, but commonly used statistical methods (e.g., multilevel modeling, structural equation modeling) at a level accessible to students who have only completed a basic first course in statistics. The rationale behind the selection of topics, assignments, and readings is described, and lessons learned from the first offering of this course are discussed.

Laboratory courses are excellent venues for students to experience psychological phenomena first-hand. In the first-year Research Methods lab at Arcadia University, students participate in weekly computerized demonstrations of classic experiments using Psytoolkit, an open-source experiment creation software. They also read original articles and answer questions relevant to course concepts (e.g. IVs and DVs). Later in the semester, student groups design novel experiments based on a lab of their choosing, modifying code, stimuli, and trial design for their investigation. Here, I introduce Psytoolkit and share teaching resources for Research Methods and content lab courses, and I showcase student projects. Psytoolkit can be paired with other open-source software like R and JASP for statistical analysis and visualization. These resources provide an alternative to pricey publisher software.

Citation practices are fundamental in methods courses. With the emergence of Artificial Intelligence (AI) technologies, students need a structured way to cite when and how AI is used. In this talk, an instructor resource, an AI Contribution Statement, which provides students with an ethical and explicit framework for reporting on AI use during idea generation and writing in research methods, will be showcased. Scaffolded activities that teach students to use and cite AI in ethical ways when writing a research proposal will be discussed. An AI Contribution Statement can be an important addition to research methods teaching to create equality in technology use and student success.

Academic Imposter Phenomenon (AIP, see Todd & McIlroy, 2025) involves feelings of insecurity and self-doubt and can adversely affect well-being, performance and persistence. In the psychology curriculum, AIP may be highest in courses perceived to be challenging. Data across two semesters showed higher AIP when students considered the context of a statistics course compared to other contexts (research methods, psychology, or general academics). Further, significant correlations were found (e.g., distress and isolation were positively correlated while growth mindset and belonging correlated negatively), with particularly interesting results for AIP in statistics. A second study involving an intervention to counter AIP through cognitive restructuring showed significant improvement for students. Use of the measure and interventions to mitigate AIP will be discussed.

Teaching introductory statistics is challenging because core ideas (e.g., sampling distributions, model predictions, sums of squares) are highly abstract. Interleaving concepts and code in Jupyter Notebooks helps visualization but often fails to build deep understanding. To address this, we redesigned Jupyter notebooks to include embodied, hands-on activities—such as shuffling paper data or drawing residuals and squares—before coding. These activities made invisible concepts tangible, exposed misconceptions, and promoted self-correction as students linked physical actions to computation. Preliminary results suggest strong learning gains: average midterm scores rose from 80% with simulation-only notebooks to 95% with the embodied redesign, demonstrating how combining computation and embodiment strengthens statistical understanding.

This presentation demonstrates two different approaches of integrating artificial intelligence into an annotated bibliography assignment in order to enhance students’ research and critical evaluation skills. In both approaches, the students summarize the articles, compare and contrast the research, and cite each reference in APA format. In the first approach, students generate an AI-produced summary of the article and then compose a brief critique of that AI-generated response, including a citation for the tool used before composing their original write-up. In the second approach, students produce the AI-generated content after writing their own annotations and then analyze the AI-produced summary. Both processes encourage students to engage with AI as a research tool while developing discernment about its accuracy, bias, and limitations. 

Our proposal examines the role of emotion regulation in attitudes towards statistics among 3rd year students. The instructor prioritizes the goal of providing a fear-free learning environment, to support students with varying levels of statistical background. This approach is informed by research demonstrating the role of emotion and emotion regulation in learning, especially in relation to academic failure (Sharabi & Roth, 2025). Preliminary analyses suggest there is considerable variability in all our measures, and that emotional regulation is positively correlated with self-report measures of resilience in statistics, self-efficacy and fear of statistics. Overall, this project may shed light on best practices for teaching of statistics and the important role emotional regulation may play on academic success.

Courses in statistics are often some of the more challenging and disliked courses in an undergraduate psychology degree, even though they are also among the most important courses. For one statistics course, thematic instruction started as an effort to find better metaphors to use in teaching statistical concepts that were unfamiliar to students. From there, the entire unit concerning hypothesis testing was transformed to where students were taught using an unfolding trial as a metaphor and narrative. After that transformation, the remaining four units of the course were also included, with different themes and accompanying narratives for each unit. Reflections and lessons learned are discussed.

Postgraduate students were taught how to administer, and interpret create psychometric tests in this course using a step-by-step, interactive methodology.  Concept mapping, item writing, EFA/CFA analysis, and client profiling were explored using combination of short theoretical lectures, practical worksheets, group presentations, and supervised practice.  Student confidence, analytical abilities, and applied competencies all improved as a result of feedback and iterative practice. The method successfully connected theory and practice, encouraging students to collaborate, communicate, and be ethically conscious. It also emphasized areas for more conversation and applications unique to HR. This method is an example of an interactive, scalable approach to teaching applied psychometrics. 

This work presents an activity to familiarize students in a psychology course with the role of probability in statistical tests. Building on the work of Addison (2017), this activity uses Zener cards. Students are allowed to run an online trial to test their extrasensory perception (ESP) ability. This activity appears very engaging and accessible. The open source statistical program JASP, and in particular the "learning statistics" module, permits to plot the binomial sample distribution for this probability events. The students have the opportunity to compare the proportion of their correct answers and to understand from a mathematical point of view whether or not they may have ESP abilities. This activity allows students to grasp the relationship between sample distributions, probability, and hypothesis testing.

This session presents two classroom-tested techniques designed to deepen statistical understanding among novice learners. The first activity uses blank normal curves and colored markers to help students visually and kinesthetically explore standard deviation units and the area under the curve. Students personalize their charts, reinforcing symmetry and percentage distributions through repetition and creativity. The second activity introduces sampling and the Central Limit Theorem using bags of Starburst candies. Students draw samples of varying sizes, record flavor distributions, and observe the emergence of predictable proportions. These tactile, collaborative experiences foster conceptual clarity and student engagement. While rooted in classic pedagogy, these tools remain highly effective and align with the needs of today’s classroom learner.

We will present ten lab modules that can be used in introductory psychology, statistics, and research methods coures. The labs can be completed with minimal resources and in a relatively short amount of class time. Each module addresses a different foundational concept in research methods and encourages critical thinking. Labs includes an experiential learning compenent that demonstrate practical applications of research methodology and statistics. All materials will be available as Open Educational Resouces (OER). The OER materials include a concise book for students, lecture videos and demonstrations, slides, data files, and instructor notes.

At small liberal arts institutions, limited resources demand creativity and flexibility in teaching and mentoring undergraduate research. This presentation explores ways to build on foundational research methods and statistics courses using scaffolded approaches for applied, collaborative research experiences that prepare students for graduate study and professional work. Using Bloom’s Revised Taxonomy, we demonstrate how our students progress from factual and conceptual understanding of research and statistics concepts to procedural and metacognitive application through faculty-mentored projects, senior thesis research, and interdisciplinary opportunities. These experiences demonstrate that mentoring research is teaching—extending foundational classroom learning into authentic, applied experiences that build critical thinking, autonomy, and self-efficacy in undergraduate scholars. 

Qualitative research holds inherent bias and requires deep reflection on the part of the researcher. During the data collection process, journaling is often used to note feelings, thoughts and bias. Developing a color coding system that depicts the researcher's initial emotional response can help with the analysis process by reminding the researcher of any potential feelings that could influence objectivity. This presentation will address how to use color coding as an initial part of the collection process and how to develop a system for future research. 

This proposal outlines two practical assignments used in a graduate statistics course for psychology students. In the first, students select a recent peer-reviewed article using a statistical method (e.g., correlation, moderation, ANOVA) and critique how it is used and reported in the Methods and Results sections. In the second, students conduct their own analysis by selecting a dataset, checking assumptions, running tests, and reporting results in APA format. To support this process, a librarian is invited to class to introduce research databases with accessible, high-quality datasets. Together, these assignments develop students’ statistical literacy, critical thinking, and applied skills.

Count data are common in psychology. This includes the number of details reported in investigative interviews or lie detection studies. These data typically show strong positive skew, with many low or zero values and relatively few large counts, violating the assumptions of traditional tests, such as ANOVAs and t-tests. Applying these traditional tests to count data can distort estimates, inflate Type I errors, obscure true effects, or produce nonsensical results. Negative binomial (NB) regression offers a more appropriate and flexible alternative, as it explicitly models discrete, overdispersed data while accommodating the lower bound at zero. We discuss why this is important, and how best to teach students through easily accessible statistical packages such as Jamovi. Students must learn analyses that match the research they conduct. 

As AI tools become more common in education, instructors need to understand how students perceive their use—especially in subjective assessments like writing. This study explores undergraduate psychology students’ perceptions of AI-generated vs. human-provided scoring and feedback. Participants reviewed feedback and scores, then completed surveys assessing their perceptions before and after learning whether the evaluator was AI or human. Factors like AI familiarity, comfort with technology, and frequency of AI use predicted more favorable attitudes. Human grading was viewed more positively overall, though misidentification influenced responses in complex ways. We discuss practical strategies for introducing AI assessment tools while supporting transparency, trust, and critical thinking.

Students in the life sciences often approach statistics with trepidation and anxiety. Statistics becomes the “dentist course”, the one they must take, know is useful, and yet would rather avoid. Many students also lack confidence in their quantitative skills. To respond, instructors may resort to simplifying course content (e.g., teaching that p < .05 means “significant” without engaging in the logic of null testing). To counter this, we present a grading policy developed by two instructors at the University of Toronto that integrates Bloom’s Taxonomy with scaffolded grading to foster engagement and strengthen conceptual mastery. This model has proven so effective among introductory undergraduates that it can support the inclusion of advanced topics such as Bayesian analysis and machine learning, without risking increased anxiety or lowered performance.  

This session introduces an art-based journaling assignment that helps undergraduate psychology students manage the emotional challenges of conducting research for the first time. Through visual and written reflections, students express feelings of uncertainty, frustration, and growth as they navigate each stage of the research process. Integrated biweekly into a Research Methods course, the assignment includes prompts such as “What does your research anxiety look like?” and encourages creativity through drawing, collage, or digital art. Peer sharing fosters empathy and normalizes struggle, while final reflections connect emotions to cognitive learning. Grounded in transformative and metacognitive theory, this technique promotes emotional resilience, self-efficacy, and inclusive learning.

Can we make statistical modeling and data wrangling meaningful to students by connecting it to real social issues? This session demonstrates a lesson that uses national data on opioid-related deaths to teach core statistical ideas, exploring and modeling variation, through a story of social justice and accountability. Participants will see how computational notebooks (R and Jupyter) can be easily implemented in a campus LMS (e.g., Canvas, Moodle) to support students in exploring multivariate data, wrangling data, and engaging in critical discussions about policy and power. The session offers practical strategies and materials for instructors interested in integrating authentic data, open-source tools, and socially relevant contexts into statistics courses.

The primary goal was to shift the focus from manual computation to conceptual understanding and research application through the use of statistical software. Students collected and analyzed their own data using SPSS, applied statistical tests to research questions, interpreted the results, and wrote APA-formatted reports. Grounded in learning science, this redesign aligns with conference themes by illustrating how relevance, active learning, and contextual practice can transform statistics into a meaningful, engaging, and transferable skill.

Statistics courses are often seen as stressful and difficult for undergraduates. While traditionally lecture-based, flipped classrooms have gained popularity for improving academic performance and student perceptions. However, they require significant time and effort to implement. This study examined whether the benefits seen in a flipped classroom could be achieved in a lecture course by incorporating key flipped elements. We found no differences in academic performance, student perception, or math anxiety between flipped and lecture courses when both included recorded lectures, opportunities for practice, and intermittent knowledge checks. This suggests flipped-classroom benefits can be replicated in traditional formats with thoughtful design.

Have you ever considered having students work with data in your classroom but weren’t sure where to start? This presentation will cover sources of open and freely available data (such as Kaggle.com, OSF.io, and many others), several considerations, a few use case possibilities, and an example assignment.  

Instructional sequence plays a critical role in learning, yet it remains unclear in practice whether direct instruction should precede or follow practice activities. This study examines whether learners’ mastery goal orientation moderates the effectiveness of instruction-first versus practice-first approaches, with changes in germane cognitive load between phases mediating the relationship between sequence and outcomes. Moderated mediation analysis shows that instructional sequence influences learning through changes in germane cognitive load, moderated by mastery goal orientation. Increases in germane cognitive load improved learning for learners high in mastery goal orientation, but hindered learning for low mastery orientation learners. These findings underscore the importance of aligning instructional design with students’ motivation, especially in early-stage statistics education.

This proposal outlines a strategy to enhance student and faculty scholarship by being involved in the Collaborative Replication and Education Project (CREP) in the context of a Senior Research Seminar at Delaware State University. While participating in CREP during Spring 2025, students formed groups to critically analyze research articles, gaining insights into the replication crisis while creating profiles on the Open Science Framework and completing IRB training. They showcased their work through authentic assessments via in-person poster presentations at the Decolonizing Psychology Conference and oral presentations at the Annual DSU Research Day. CREP fosters essential research skills and collaboration, particularly benefiting students of color, while promoting an inclusive educational environment and connecting students with faculty mentors.

We describe an activity for psychology undergraduate courses to explore key concepts related to analytical flexibility using registered reports. Students read a first-stage registered report, receive the data, and implemented the analysis plan as described. Students give a 10-minute oral presentation of their results, then compare with the final Stage 2 and other students. Students are able to identify sources of analytical flexibility in the registered report. Students are cautious of results which did not support main hypotheses. This activity helps students to achieve multiple important learning outcomes focused on the impact of analytical flexibility, advantages and limitations of open science practice, and using statistical methods with real data. This type of project could be integrated into advanced undergraduate or graduate research methods, statistics, or lab courses. 

Estimation statistics (effect sizes, confidence/credible intervals, and meta-analysis) provides an important supplement to testing approaches.  This presentation will discuss integration of estimation into your teaching, emphasizing estimates from non-parametric models.  Introducing students to median-based effect sizes is critical, as effect sizes from parametric models are not appropriate for many types of data (e.g. reaction times).  The estimation lens makes this easy for students, as they can directly see the connections between parametric (means and mean differences) and non-parametric effect sizes (medians and median differences).  We’ll look at 3 example designs and data sets you can use to teach median-based effect sizes, working from the simple one-group design up to 2x2 factorial designs.  We’ll obtain these effect sizes using esci, available in R, JASP, and jamovi.

To cover more topics in a semester and make courses less threatening for students, some psychologists have advocated for a consumer-based approach to teaching introductory statistics that removes any requirement of students actually producing statistics. But can students become statistically literate, critical consumers of statistics by only talking about statistical concepts? This question is considered through the lens of Kolb’s Experiential Learning Theory, which is utilized extensively in the teaching of other concepts in other areas of psychology. Reflections from a conversation with senior methodologists about how they gained their expertise in quantitative methods, a consideration of how producing statistics can lead to higher statistical literacy, and concrete examples of concepts that benefit from having the students produce statistics using empirical data are presented.

“Association is not Causation” and “Bad Survey” are two assignments in a first-year research Methods course that connect to everyday experience. In “Association is not Causation,” students choose a report from the news (e.g., mainstream or social media) that makes causal claims derived from an association. Students identify the claims, come up with alternative explanations, and design an experiment that could test causality. For the “Bad Survey,” assignment, students critique and re-write questions from a survey they have encountered. Each student chooses one of the two assignments to present in class in an interactive format (e.g., asking the class to identify claims, alternative explanations, question flaws, etc.). The low-stakes presentation gives students experience with this important skill, and the interactive nature builds a classroom habit of collaboration and discussion.

Most statistics tests reward recall and procedural skills, yet rarely measure whether students can use data to answer real questions. We developed a one-hour performance assessment for introductory statistics that emphasizes data reasoning, transfer, and communication over memorization. Co-designed with teachers and delivered after Chapter 9 of the CourseKata curriculum, the assessment asks students to explore data, build and evaluate models, and justify their interpretations. Early evidence suggests this approach surfaces students’ thinking more authentically, promotes curiosity, and aligns assessment with real-world data use. We will share sample tasks, scoring guides, and lessons learned for instructors seeking more meaningful assessment practices in statistics.

Interactive demonstrations in research methods and statistics take the abstract technical elements of the material (those often associated with dwindling engagement) and make them accessible and meaningful. Multiple tools are available to collect real-time student data in class (iClicker, slido), but these offer limited flexibility in how findings are displayed and rarely connect to statistical analysis. Using streamlined R code, this presentation will demonstrate how real-time in-class data collection is augmented by analytical software to unpack results. I will include multiple course demonstrations of study designs and corresponding statistical phenomena to showcase the pedagogical value of this approach. Audience members can serve as live participants, multiple code templates will be disseminated, and metrics of corresponding course engagement will be shared.

As technology transforms research and practice, psychology education must strengthen students’ statistical reasoning, literacy, and ethical awareness. This proposal introduces a brief pedagogical activity that integrates artificial intelligence (AI) and visualization tools such as Tableau Public into introductory statistics courses. Over one to two class sessions, students analyze real-world psychological datasets in Excel or SPSS with AI guidance to identify variables, manage missing data, conduct analyses, and compare AI-generated interpretations with SPSS results. They then design Tableau dashboards to visualize behavioral trends and tell data-driven stories. The activity promotes critical thinking, ethical reflection, and evidence-based communication—core competencies for psychological research and professional practice (Batt et al., 2020; Vieriu, 2025).

Two activities teach AI skills to facilitate effective scientific writing. The first focuses on AI use early in the literature review process. In small groups, students use AI to generate keywords and source research articles, then discuss the quality and accuracy of its responses. Then, they can choose to independently use AI for appropriate and ethical support as they write the literature review. In the second activity, students request individualized feedback on their literature review from AI. Students identify how prompt engineering can improve feedback on scientific writing and compare AI feedback to faculty feedback. Combined, students learn to use AI at multiple stages of the literature review process, which can generalize to other stages of scientific writing. Students gain a clearer understanding of its strengths and limitations while becoming thoughtful, critical users of AI.

This poster showcases a thematic redesign of Social Psychology that uses travel and tourism as a lens for exploring human behavior. While the theme is global, students in Hawaiʻi engage deeply by observing how tourism shapes local life. Using Padlet and Perusall, students collaboratively analyze research on topics such as travel selfies, voluntourism, and prejudice reduction through travel. Eight travel-themed Research Activities immerse them in authentic methods, from interviews to content analysis. Student reflections reveal stronger engagement and understanding of social psychology as it appears in real life. This thematic, place-aware approach demonstrates how travel can serve as a vehicle for active, inclusive, and research-centered learning.

Those in favor of artificial intelligence (AI) have suggested that generative AI can save instructor time by creating test questions. However, there is not enough data to suggest questions written by generative AI or instructors are more or less difficult for students. Thus, we generated a test bank of questions that were written by an instructor and Microsoft Copilot. These questions were presented to graduate students at the start and end of the academic term, and they also provided confidence ratings for each. Data are still being collected for the current academic term.

Are you bored of the problems and examples that you use in your statistics class, but feel like you’re stuck in a creative rut?  This session will explore clever ways in which you can use AI to help you to break out of this rut in order to turn your boring numbers-only problems into imaginative applied class examples, activities, exercises, test questions, and more.  Come chat about how to use resources like Chat GPT, Gemini, and Canva to find your groove and get students excited about your material!  We’ll explore possibilities and discuss ways to avoid common pitfalls so that you can jump right into a path toward a more engaging course!

Are you looking for a well-validated measure to use in class demonstrations or statistics/research method projects? Consider the Wooster-Wickline College Adjustment Test, Second Version (WOWCAT-2). This free 100-item measure has 10 subscales that can be used together or separately: Mental Health, Social Involvement, Substance Abuse, Family Support, Loneliness, Time Management, Living Arrangements, Academic Performance/Study Habits, Autonomy, and Coping/Resources. The measure demonstrated internal consistency, content validity, convergent validity, and incremental validity in two very different college samples. It is engaging to students because it is relevant, allowing a wide array of options for analysis and practice for entry-level statistics, such as t-test, Cronbach’s alpha, correlation, and regression (sample data set available with permission of author).

“Research is fun!” This phrase may ring true for faculty, but making this a reality for our students can be challenging. This presentation will elaborate on creative assignments and games I use to make research fun for students. For example, students can do scavenger hunts to correct APA writing errors or write research-related song lyrics. Poster presentations are showcased at a department event called the Research Celebration. From the first day of class, to the Research Celebration, the message is: “research is fun!” Learning science points to the importance of play in the classroom, so the aim of this presentation is to give practical examples and exchange ideas of how instructors can use fun and games in their required research and statistics courses. 

To create an authentic assessment of student research competencies, I developed a new assignment for my Lab in Learning & Motivation class at Northeastern University, adapted from the Passion-Driven Statistics course from Wesleyan University. Nineteen psychology majors posed a research question of their own choosing and answered it using variables from the AddHealth dataset. They worked for one month on the project, producing research posters as the assessment measure. We incorporated Claude.ai in the brainstorming process by asking it to identify variables in the AddHealth study that might be related to my students’ questions. The students confirmed that the variables were present by examining the codebooks. This gave students a supervised in-class opportunity to use the AI as a “research assistant.” 

This session explores how visual learning strategies enhance comprehension and retention of abstract statistical concepts. By engaging students in metacognitive practices, these strategies foster self-directed learning and higher-order thinking. Participants will be introduced to “Mindsketching,” a technique that helps learners visually represent and articulate complex ideas in their own words. The session will highlight research supporting visual learning and demonstrate how these strategies can be applied across teaching modalities and adapted to diverse statistical content. Attendees will leave with practical tools to promote deep learning, conceptual clarity, and meaningful knowledge transfer in quantitative coursework.

This presentation highlights an innovative approach to teaching a four week synchronous online Research Methods course meeting twice weekly for nearly three hours. To sustain engagement and accountability in this condensed format, interactive in class activities such as graded Kahoot warm ups, application based exercises, and reflective exit tickets were woven throughout each session. These activities encouraged continuous participation while keeping grading manageable and allowing for individualized feedback and targeted support. Student feedback reflected surprise at how engaging and accessible the material felt, particularly among those who had previously struggled or failed the course. The resulting classroom community showed that even in intensive online formats, thoughtful structure and active learning can transform motivation and understanding in research methods education.

It is common for clinical mental health counseling students to have little to no desire to conduct research professionally. The goal of this newly implemented research assignment was three-fold: 1) address AI concerns experienced in higher education by eliminating a typical research paper, 2) encourage abstract and hypothetical thought and, 3) mentor students in the development of a research study that suits their area of future career. Students are encouraged to develop a hypothetical research study from start to finish by submitting weekly assignments on their progress, including relevant literature reviews, descriptions of the selected variables and populations, and their proposed methodology. Weekly feedback and group discussion facilitate the development of this proposed hypothetical research study.

Students often struggle with many of the concepts in inferential statistics including type one and type two error, p value and null and alternative hypotheses. In my courses, I have had near universal comprehension scores on these concepts by teaching them alongside a more well known cultural reference, namely the criminal justice system. In this presentation, I will walk instructors through the surprising relationship between statistical  and criminal justice concepts that can be easily adapted to help your students learn them beyond a reasonable doubt!

Students’ typical first research experience often involves supporting graduate students or faculty on their projects. While effective at exposing them to systematic rigor, it fails to produce the elation one feels when studying their own research question. Furthermore, granting students autonomy to conduct their own research, and early on, may support the development and refinement of their research interests, smoothing their transition to graduate school. To expose underclassmen to the joys of research, we piloted a course-based undergraduate research experience in survey design and analysis with 25 students in which they all studied their own research question. In this talk we share our lessons learned in terms of the feasibility of the course scale, the impact and experience of the students, and the role such initiatives can play in our departments. 

Metacognitive reflections are useful as they provide students with an opportunity to reflect and evaluate their approach to learning in a course, recognize ineffective strategies, and improve plans to learn (Merkebu et al., 2024). We examined how students’ metacognitive reflections changed across a semester and the relation to performance. Students in a large introductory statistics course were asked to complete a unit wrapper after each of their summative assessments. The reflections focused on students’ perceptions of their performance, study strategies used, roadblocks faced, and reflections on change in behavior for the next assessment.Planned analyses examined the association between perceived grades and actual grades, determining if the number of roadblocks faced are associated with actual grades, and determine which study strategies are most successful in the large lecture course.

I would like to provide my colleagues who are new to teaching the sequence of research methods and experimental design with a model that I have been using. With this model, in research methods students develop a research proposal and in experimental design they collect their data, analyze their data, write the result and discussion sections of their manuscript. I will share my Canvas courses so those who are new to teaching these courses can easily adopt my materials. 

This paper presents an experiential approach to teaching Research Methods, immersing students in the research process as practiced by social scientists. After completing human subject protection training, students develop research questions using concept mapping tools and collaboratively select a research topic (e.g., gender differences in mate selection, college students' anxiety). They create survey questions, complete the survey themselves, and use their responses as the dataset for analysis in SPSS. The course culminates in interpreting results and writing a research paper grounded in empirical evidence. This hands-on journey not only demystifies the research process but also fosters a deeper connection to the relevance of research methods, offering a transformative learning experience.

Teaching research methods can be challenging because students often find the content intimidating or irrelevant (Balloo et al., 2018; Earley, 2014). To make learning more meaningful, I wove students’ research interests into class examples in my two sections of Research Methods 1. On the first day, students identified research interests, variables, or populations, which I incorporated throughout the semester. At semester’s end, students (n=10) completed an anonymous Qualtrics survey using 7-point Likert-type scales. One-sample t-tests showed ratings significantly above the midpoint (all p≤ .01). Students reported that this approach enhanced engagement and motivation, improved learning, and better prepared them for Research Methods 2. The poster will present findings and practical recommendations for instructors seeking an engaging teaching strategy.