Qualitative Prospectus Template

Qualitative Prospectus Instructions for Learners

General Instructions

Additional Information for Completing Each Slide

Use this template only if you have a Qualitative topic.

This is a working document. You will work on and revise this PPT starting in year one of your program up through x-955.

Instructions per Course Type:

Research (RES) Courses: Refer to your course syllabus to determine which slides you should complete or revise.

Residency (RSD) Courses & Dissertation:

RSD-851 - complete slides in RSD1 section.

RSD-883/881 & x-955 – revise/update slides from RSD1 and complete slides in RSD2 section.

Requirements, hints, and alignment notes are found in the Speaker Notes section.

To view speaker notes, click the “View” tab at the top of the application and select “notes.”

Hint: You may need to expand the notes section in order to see all of the notes contained for each slide.

To view comments/feedback from faculty, click the “review” tab at the top of the application and select “Show Comments.”

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Instructions for Faculty

General Instructions

Additional Information

Written feedback is to be provided via bubble comments.

Comments can be created by holding Ctrl+M (for PC) or Command+Shift+M (Mac) on your keyboard, or via the Review tab.

To access the Comment pane, click the “review” tab and select “Show comments.”

The notes section in each slide contains the slide requirements.

Feedback should be focused on helping the learner meet the slide requirements.

See the supplementary faculty job aid materials for grading and other resources.

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The Integration of Adaptive Learning Technologies in Traditional Classroom Environments

Guerline Pierre Joseph

Dr. Jacobs

8/28/2024

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Alignment Table

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This alignment table demonstrates the coherence of my research design. The problem statement highlights the gap in our understanding of effectively integrating adaptive learning technologies in classrooms. The purpose statement outlines my intention to explore this integration process across various educational contexts. The research questions are designed to investigate the experiences of educators and students and the factors influencing successful implementation. The qualitative methodology, specifically a multiple case study design, aligns with these elements by allowing for an in-depth exploration of the complex processes and contextual factors involved in integrating adaptive learning technologies.

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Literature Review: Background to the Problem

Evolution of adaptive learning technologies (1960s to present)

Current state: Improved outcomes but implementation challenges (Vincent-Ruz & Boase, 2022)

Accelerated adoption post-COVID-19 (Jing et al., 2023)

Need for research on long-term impacts and best practices (Harati et al., 2021)

Gap: Lack of understanding on effective integration in diverse contexts (Cebrián et al., 2020)

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Adaptive learning technologies have evolved significantly since their inception in the 1960s. Initially developed as basic computer-assisted instruction, these systems have grown more sophisticated with artificial intelligence and learning analytics advancements. Research has shown potential benefits, including improved learning outcomes and increased student engagement (Vincent-Ruz & Boase, 2022). However, implementation and integration challenges with existing curricula persist (Harati et al., 2021). The COVID-19 pandemic accelerated adoption, but questions remain about long-term impacts and best practices for classroom integration (Jing et al., 2023). This background sets the stage for understanding the current state of adaptive learning technology in education and the need for further research.

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Literature Review: Problem Space

State of the problem: Limited understanding of effective integration strategies

Known aspects: Potential for improved learning outcomes and personalization

Unknown aspects: Best practices for implementation across diverse contexts

Directions for future research: Long-term impacts, teacher roles, equity considerations

Synthesis: There is a need for comprehensive framework to guide effective integration

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The problem space for this study emerged from several key areas identified in recent literature. These include the need for effective integration strategies across diverse subjects and contexts (Cebrián et al., 2020), limited understanding of long-term impacts on student outcomes and self-regulated learning skills (Harati et al., 2021), and the effect of adaptive technologies on teacher roles and pedagogical practices (Toth et al., 2021; Khan & Khojah, 2021). Additionally, there's a need for best practices in designing systems that address diverse learning needs (Gligorea et al., 2023) and ethical considerations for equitable implementation (Li et al., 2021). These areas highlight the complexity of the problem and the need for comprehensive research to guide effective implementation.

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Literature Review: Theoretical Foundations

Self-Directed Learning (SDL) framework (Toth et al., 2021)

Adaptive learning theory (Gligorea et al., 2023)

Cognitive load theory (Bradáč et al., 2022)

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This study is grounded in three key theoretical frameworks. The Self-Directed Learning (SDL) framework, as employed by Toth et al. (2021), provides a structure for examining how adaptive technologies influence learner autonomy. Adaptive learning theory, discussed by Gligorea et al. (2023), offers insights into personalized instruction and its impact on learning outcomes. Cognitive load theory, explored by Bradáč et al. (2022), helps us understand how these technologies can optimize cognitive resources. Together, these theories provide a comprehensive lens through which to examine the integration of adaptive learning technologies in classroom environments, considering aspects of learner autonomy, personalization, and cognitive processing.

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Literature Review: Review of Literature

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The literature review covers five key themes: effectiveness of adaptive learning technologies, integration in classroom environments, design principles, data analytics and personalization, and ethical considerations. Studies have shown mixed results in effectiveness across different contexts (Vincent-Ruz & Boase, 2022). Integration challenges include teacher training and curriculum alignment (Cebrián et al., 2020). Design principles focus on user experience and pedagogical frameworks (Gligorea et al., 2023). Data analytics raises questions about privacy and decision-making processes (Jing et al., 2023). Ethical considerations include equity of access and potential biases in adaptive systems (Li et al., 2021). This review highlights the multifaceted nature of the research problem and informs our study design.

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Problem Statement

It is not known how to effectively integrate adaptive learning technologies with traditional classroom instruction to optimize student learning outcomes and address diverse learner needs across various educational contexts and subject areas.

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The problem statement encapsulates the core issue this study aims to address: "It is not known how to effectively integrate adaptive learning technologies with traditional classroom instruction to optimize student learning outcomes and address diverse learner needs across various educational contexts and subject areas." This statement reflects the gap in current knowledge about practical implementation strategies that can maximize the benefits of adaptive learning while navigating the complexities of diverse educational settings. By focusing on the "how" of integration, this study aims to provide actionable insights for educators and policymakers to improve the implementation of adaptive learning technologies in classrooms.

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Phenomenon

The process of integrating adaptive learning technologies in traditional classroom environments, including:

Implementation strategies

Stakeholder experiences (educators, students, administrators)

Challenges and opportunities

Impact on teaching and learning practices

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The central phenomenon under investigation is the process of integrating adaptive learning technologies in traditional classroom environments. This complex process involves multiple stakeholders, including educators, students, and administrators, and is influenced by various factors such as technological infrastructure, pedagogical approaches, and institutional policies (Toth et al., 2021). By focusing on this phenomenon, we aim to uncover the nuanced interactions between technology and pedagogy, the challenges and opportunities that arise during implementation, and the strategies that lead to successful integration across different educational contexts. This comprehensive exploration will provide a holistic understanding of the integration process

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RQ1: How do educators experience the integration of adaptive learning technologies in their classroom instruction?

RQ2: What factors contribute to the successful implementation of adaptive learning technologies in diverse educational contexts?

RQ3: How do students perceive and engage with adaptive learning technologies in traditional classroom settings?

Research Questions

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Our research questions are designed to explore different aspects of the central phenomenon. RQ1 focuses on educators' experiences, aiming to uncover the challenges, strategies, and perceptions involved in integrating adaptive learning technologies. RQ2 seeks to identify the factors that contribute to successful implementation, which could include institutional support, professional development, or technological infrastructure. RQ3 examines the student perspective, exploring how learners engage with and perceive these technologies in their traditional classroom settings. Together, these questions provide a comprehensive approach to understanding the integration process from multiple angles, ensuring that we capture the phenomenon's complexity from various stakeholder perspectives.

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Methodology Justification

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The choice of a qualitative methodology aligns with the exploratory nature of this research. Qualitative methods are particularly suited for understanding complex phenomena in their natural settings and exploring the meanings individuals ascribe to their experiences (Creswell & Poth, 2024). This approach allows us to capture the nuanced interactions between technology, pedagogy, and learning environments that are central to our research problem. While quantitative methods offer valuable insights in many educational research contexts, they are less suitable for addressing our "how" questions and exploring the depth of understanding needed for this complex integration process (Hodge, 2020). The qualitative approach enables us to uncover unexpected themes and insights crucial for studying innovative educational practices.

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Design

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We've chosen a multiple case study design for this research (Hunziker & Blankenagel, 2024). Case studies allow for an in-depth examination of a bounded system over time, making them ideal for exploring the complex process of adaptive learning technology integration across various educational contexts. This design enables us to investigate multiple cases, comparing and contrasting experiences across different settings. Other qualitative designs were considered but deemed less suitable. For example, phenomenology focuses more on lived experiences than on processes, while grounded theory aims to develop a new theory, which is not our primary goal. The multiple case study design provides the flexibility and depth needed to address our research questions comprehensively.

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Feasibility Slide 1

Resources for Study

Ethical Concerns

Access to educational institutions implementing adaptive learning technologies

Recording equipment and analysis software

CITI training completion

Minimal risk to participants

Confidentiality and data protection measures

Informed consent procedures

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Ensuring the feasibility of this study is crucial. We've identified key resources needed, including access to educational institutions implementing adaptive learning technologies, recording equipment for interviews and observations, and analysis software for data processing. Ethical concerns have been carefully considered. The study poses minimal risk to participants, and we've developed robust confidentiality and data protection measures. Informed consent procedures will be rigorously followed. We'll complete CITI training to ensure ethical research practices. These considerations demonstrate that our study is not only feasible but also designed to protect participants and maintain high ethical standards throughout the research process.

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Feasibility Slide 2

Study Alignment with Program

(Identify Program of Study)

Feasibility Concerns

Degree & Emphasis: Doctor of Education in Organizational Leadership

Alignment: Focus on educational technology integration and leadership in implementing innovative practices

Potential challenges in accessing diverse educational settings

Backup plan: Utilize professional networks and online recruitment

Study is feasible with appropriate planning and flexibility

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This study aligns well with my Doctor of Education program in Organizational Leadership. The focus on educational technology integration and leadership in implementing innovative practices directly relates to the program's emphasis on organizational change and innovation. In terms of feasibility concerns, we've identified potential challenges in accessing diverse educational settings. To address this, we've developed a backup plan utilizing professional networks and online recruitment strategies. Given the importance of the topic and the careful planning we've done, including addressing potential obstacles, we believe this study is feasible. We're prepared to be flexible and adapt our approach as needed to ensure successful completion of the research.

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Defend

Questions

Feedback

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Objectives:

This slide is a placeholder for your defense of your topic to your residency instructor, peers, and/or dissertation committee.

Learners should be prepared to answer questions about their study, including the key points, alignment, and feasibility.

Slide Requirements:

This slide is for presentation purposes in RSD-851 only – no content is required.

After successful completion of RSD-851, this slide may be deleted.

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Next Steps

Refine research design based on feedback

Begin literature review for Chapter 2

Identify potential sites and participants

Develop data collection instruments

Submit for IRB approval

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Moving forward, our next steps are crucial for the successful execution of this study. We'll begin by refining our research design based on the feedback received today. The literature review for Chapter 2 will be expanded and updated to ensure a comprehensive foundation. We'll then focus on identifying potential sites and participants, leveraging our professional networks and the criteria we've established. Concurrently, we'll develop our data collection instruments, including interview protocols and observation guides. Finally, we'll prepare and submit our IRB application to ensure all ethical considerations are addressed before beginning data collection. This systematic approach will set a strong foundation for our research.

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Purpose Statement

The purpose of this qualitative multiple case study is to explore the processes, challenges, and strategies involved in integrating adaptive learning technologies within traditional classroom environments across K-12 and higher education settings, in order to develop a comprehensive framework for effective implementation that optimizes student learning outcomes and addresses diverse learner needs.

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The purpose statement encapsulates the core aim of our research: to explore the processes, challenges, and strategies involved in integrating adaptive learning technologies within traditional classroom environments. By focusing on both K-12 and higher education settings, we aim to develop a comprehensive framework for effective implementation that can be applied across various educational contexts. This study seeks to optimize student learning outcomes while addressing the diverse needs of learners. The multiple case study approach will allow us to gain in-depth insights into the integration process, considering the perspectives of educators, students, and administrators. This comprehensive exploration will contribute valuable knowledge to the field of educational technology and adaptive learning.

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Population, Target Population, & Sample

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Our study focuses on educators and students in K-12 and higher education institutions as the general population. The target population narrows to those in schools that have implemented adaptive learning technologies for at least one academic year, ensuring participants have sufficient experience with these systems. We aim for a sample of 20-30 participants, evenly split between educators and students, across 4-6 educational institutions. This sample size aligns with recommendations for qualitative case studies and should allow us to reach data saturation. The diverse range of participants across multiple institutions will provide a rich dataset for understanding the integration process in various contexts.

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Instrumentation & Data Sources

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We will use three primary data sources to ensure a comprehensive understanding of the phenomenon. Semi-structured interviews with educators and students will provide in-depth insights into their experiences and perceptions. These interviews will allow for flexibility in exploring emerging themes while maintaining consistency across participants. Classroom observations will offer direct examination of how adaptive learning technologies are used in practice, capturing the nuances of implementation that may not be verbalized in interviews. Document analysis of lesson plans and technology integration plans will provide context on implementation strategies and institutional approaches. This triangulation of data sources will enhance the credibility and richness of our findings.

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Data Collection Steps: Slide 1 Required Permissions

Required permissions/approvals (prior to data collection)

Site approval from participating institutions

IRB approval from Grand Canyon University

Informed consent from all participants

Permission to use and adapt interview protocols

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Before data collection begins, we must obtain several key permissions and approvals. We'll secure site approval from each participating institution, ensuring we have official authorization to conduct our research. IRB approval from Grand Canyon University is crucial to ensure our study meets all ethical standards. We'll obtain informed consent from all participants, clearly explaining the study's purpose, procedures, and any potential risks or benefits. Permission to use and adapt interview protocols will be secured if we're basing our instruments on existing tools. We'll also complete any required administrative processes and obtain validation information on our instruments. These steps are essential for conducting ethical, authorized research.

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Data Collection Steps: Slide 2 Sampling Strategy and Sample Selection

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Our sampling strategy focuses on purposive sampling to select information-rich cases that can provide deep insights into our research questions. The primary criteria for selection include educators with at least one year of experience using adaptive learning technologies and students currently using these technologies in their classes. This ensures participants have sufficient exposure to reflect on their experiences. As a backup plan, we'll employ snowball sampling through professional networks if we face challenges in recruiting participants. This approach allows us to adapt our recruitment strategy while still maintaining the focus on participants with relevant experience. The flexibility in our sampling approach increases the feasibility of our study.

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Data Collection Steps: Slide 3 Collecting the Data

Contact potential sites and participants

Obtain informed consent

Conduct semi-structured interviews (60-90 minutes each)

Perform classroom observations (2-3 per educator)

Collect relevant documents

Maintain field notes and reflections

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Our data collection process will follow a systematic approach. We'll begin by contacting potential sites and participants, explaining the study and its significance. Once participants are identified, we'll obtain informed consent, ensuring they understand their rights and the study procedures. Semi-structured interviews, lasting 60-90 minutes each, will be conducted with both educators and students. We'll perform 2-3 classroom observations per educator to capture a range of experiences with adaptive learning technologies. Relevant documents will be collected from participants and institutions to provide context. Throughout the process, we'll maintain detailed field notes and reflections to capture additional insights and contextual information. This multifaceted approach will provide a rich dataset for analysis.

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Data Collection Steps: Slide 4 Data Management and Storage

Where will you store the data?

Store data on password-protected, encrypted hard drive

How long will you store the data?

Keep data for 3 years post-study completion

How will you protect the data?

Use pseudonyms to protect participant identities

How will you destroy the data?

Destroy data through secure deletion and physical destruction of storage media

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Ensuring proper data management and storage is crucial for maintaining the integrity and confidentiality of our research. All collected data will be stored on a password-protected, encrypted hard drive to prevent unauthorized access. We'll retain the data for 3 years post-study completion, in line with ethical guidelines and to allow for potential follow-up analyses. To protect participant identities, we'll use pseudonyms in all research documents and publications. After the retention period, data will be destroyed through secure deletion methods and physical destruction of storage media. These measures demonstrate our commitment to protecting participant privacy and maintaining the ethical standards of our research.

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Data Analysis Steps: Slide 1 Data Source #1 – Analysis Strategy

Data Source #1 – Semi-structured Interviews:

Transcribe interviews verbatim

Conduct thematic analysis (Kiger & Varpio, 2020)

Develop initial codes

Identify themes and subthemes

Review and refine themes

Define and name themes

Produce report of findings

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For our semi-structured interviews, we'll employ a rigorous thematic analysis approach following Braun and Clarke's (2006) six-step process. First, we'll transcribe interviews verbatim to ensure accuracy. We'll then familiarize ourselves with the data through repeated reading. Initial codes will be generated, followed by searching for themes within these codes (Kiger & Varpio, 2020). We'll review and refine these themes, ensuring they accurately represent the data. Once themes are defined and named, we'll produce a report of our findings. This systematic approach allows us to identify patterns and insights across our diverse set of interviews, providing a comprehensive understanding of participants' experiences with adaptive learning technology integration.

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Data Analysis Steps: Slide 2 Data Source #2 – Analysis Strategy

Data Source #2 – Classroom Observations and Document Analysis:

Review observation notes and documents

Conduct content analysis

Identify patterns and themes

Cross-reference with interview data

Synthesize findings across all data sources

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For our classroom observations and document analysis, we'll employ a content analysis approach. We'll begin by thoroughly reviewing observation notes and collected documents. Through this process, we'll identify patterns and themes related to the implementation and use of adaptive learning technologies. These findings will be cross-referenced with our interview data to triangulate our results and ensure consistency across data sources. This multi-method analysis approach allows us to capture both the stated experiences of participants and the observed realities of technology integration. By synthesizing findings across all data sources, we can develop a comprehensive understanding of the adaptive learning technology integration process in diverse educational settings.

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Feasibility Slide 1

Resources for Study

Ethical Concerns

Access to educational institutions implementing adaptive learning technologies

Recording equipment (audio/video) for interviews and observations

Qualitative data analysis software (e.g., NVivo)

Secure data storage solutions

CITI training completion for ethical research conduct

Minimal risk to participants

Confidentiality and data protection measures, including use of pseudonyms and secure data storage

Informed consent procedures for all participants, including parental consent for minors if applicable

Measures to prevent disruption of regular educational activities during observations

Protocol for handling sensitive information that may arise during interviews or observations

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Ensuring the feasibility of our study is crucial for its success. We've identified key resources needed, including access to educational institutions, recording equipment, and analysis software. Ethical concerns have been carefully addressed to protect our participants. The study poses minimal risk, and we've developed robust confidentiality and data protection measures. Informed consent procedures will be rigorously followed, and we'll complete CITI training to ensure ethical research practices. By carefully considering these aspects, we've demonstrated that our study is not only feasible but also designed to maintain high ethical standards throughout the research process.

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Feasibility Slide 2

Study Alignment with Program

(Identify Program of Study)

Feasibility Concerns

Degree & Emphasis: Doctor of Education in Organizational Leadership

Alignment: This study focuses on educational technology integration and leadership in implementing innovative practices, directly aligning with the program's emphasis on organizational change and innovation in educational settings.

Potential challenges in accessing diverse educational settings across K-12 and higher education

Backup plan: Utilize professional networks, educational technology forums, and snowball sampling for recruitment

Time constraints for data collection across multiple sites

Potential for participant attrition, especially for longitudinal aspects of the study

Strategies to ensure consistent data collection across different educational contexts

The study is deemed feasible with appropriate planning, flexibility in recruitment strategies, and careful time management

Preparedness to adapt research protocols in response to unforeseen challenges or opportunities that may arise during the study

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Our study aligns closely with the Doctor of Education program in Organizational Leadership, focusing on educational technology integration and leadership in implementing innovative practices. This alignment ensures that our research contributes meaningfully to both the field and our academic development. We've identified potential challenges, such as accessing diverse educational settings, and developed backup plans including utilizing professional networks and online recruitment strategies. Based on our careful planning and consideration of potential obstacles, we believe this study is feasible. We're prepared to be flexible and adapt our approach as needed to ensure successful completion of the research, demonstrating our readiness to undertake this important study.

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Defend

Questions

Feedback

DOCTORATES WITH PURPOSE

Objectives:

This slide is a placeholder for your defense of your topic to your residency instructor, peers, and/or dissertation committee.

Learners should be prepared to answer questions about their study, including the key points, alignment, and feasibility.

Slide Requirements:

This slide is for presentation purposes in RSD-881/883 only – no content is required.

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Next Steps

Finalize Chapters 1-3 of the dissertation

Prepare for and submit IRB application

Develop detailed data collection protocols

Identify and contact potential study sites

Begin participant recruitment process

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As we move forward, our next steps are crucial for the successful execution of this study. We'll begin by finalizing Chapters 1-3 of the dissertation, incorporating feedback from this presentation. Preparing and submitting the IRB application will be a priority to ensure all ethical considerations are addressed. We'll develop detailed data collection protocols, including interview guides and observation checklists. Identifying and contacting potential study sites will commence, leveraging our professional networks and established criteria. We'll also initiate the participant recruitment process, ensuring a diverse and representative sample. These steps will set a solid foundation for the data collection phase of our research.

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References

Bradáč, V., Smolka, P., Kotyrba, M., & Průdek, T. (2022). Design of an intelligent tutoring system to create a personalized study plan using expert systems. Applied Sciences, 12(12), 6236. https://doi.org/10.3390/app12126236

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa

Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D., & Walker, K. (2020). Purposive sampling: Complex or simple? Research case examples. Journal of Research in Nursing. https://doi.org/10.1177/1744987120927206

Cebrián, G., Palau, R., & Mogas, J. (2020). The Smart Classroom as a Means to the Development of ESD Methodologies. Sustainability, 12(7), 3010. https://doi.org/10.3390/su12073010

Creswell, J. W., & Poth, C. N. (2024). Qualitative inquiry and research design: Choosing among five approaches. (5th Ed.) Sage publications.

Fischer, H. E., Boone, W. J., & Neumann, K. (2023). Quantitative research designs and approaches. In Handbook of research on science education (pp. 28-59). Routledge.

Gligorea, I., Cioca, M., Oancea, R., Gorski, A. T., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13(12), 1216. https://doi.org/10.3390/educsci13121216

Harati, H., Sujo-Montes, L., Tu, C. H., Armfield, S. J., & Yen, C. J. (2021). Assessment and learning in knowledge spaces (ALEKS) adaptive system impacts students' perception and self-regulated learning skills. Education Sciences, 11(10), 603. https://doi.org/10.3390/educsci11100603

Hodge, S. R. (2020). Quantitative research. In Routledge Handbook of Adapted Physical Education (pp. 147-162). Routledge.

Hunziker, S., & Blankenagel, M. (2024). Multiple case research design. In Research Design in Business and Management: A Practical Guide for Students and Researchers (pp. 171-186). Wiesbaden: Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-34357-6_9

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References Continued

Jing, Y., Zhao, L., Zhu, K., Wang, H., Wang, C., & Xia, Q. (2023). Research Landscape of Adaptive Learning in Education: A Bibliometric Study on Research Publications from 2000 to 2022. Sustainability, 15(4), 3115. https://doi.org/10.3390/su15043115

Khan, M. A., & Khojah, M. (2021). Artificial Intelligence and Big Data: The Advent of New Pedagogy in the Adaptive E-Learning System in the Higher Educational Institutions of Saudi Arabia. Education Research International, 2022(1), 1263555. https://doi.org/10.1155/2022/1263555

Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131. Medical teacher, 42(8), 846-854. https://doi.org/10.1080/0142159X.2020.1755030

Li, F., He, Y., & Xue, Q. (2021). Progress, challenges, and countermeasures of adaptive learning. Educational Technology & Society, 24(3), 238-255. https://doi.org/10.30191/ETS.202107_24(3).0017

Shih, S. C., Chang, C. C., Kuo, B. C., & Huang, Y. H. (2023). Mathematics intelligent tutoring system for learning multiplication and division of fractions based on diagnostic teaching. Education and Information Technologies, 28(7), 9189-9210. https://doi.org/10.1007/s10639-022-11553-z

Toth, J., Rosenthal, M., & Pate, K. (2021). Use of adaptive learning technology to promote self-directed learning in a pharmacists' patient care process course. American Journal of Pharmaceutical Education, 85(1), 7971. https://doi.org/10.5688/ajpe7971

Vincent-Ruz, P., & Boase, N. R. (2022). Activating discipline specific thinking with adaptive learning: A digital tool to enhance learning in chemistry. Plos one, 17(11), e0276086. https://doi.org/10.1371/journal.pone.0276086

Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE Publications

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