Instructional Design/Program Development

Unlocking Student Success with Digital Learning Logs 

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This post is provided by guest blogger, Samerah Saad, graduate student University of St. Francis, MS Talent Development program. 

Arizmendi et al. (2023) highlights how Learning Management Systems and utilization of digital logs can provide insights to student performance and outcomes. This would be utilized to assess e-learning environments and their ability to understand the unique interaction patterns for the student. Moreover, this would also be used to analyze the data to calculate trends among all class members. This will be helpful to see how long each student is spending on coursework in comparison to the grades they are earning. The authors highlight how essential it is to enhance the learning experience through diverse learning approaches, standardization, instructional strategies, and proper use of technological tools/resources. 

I recommend this article’s approach to put into practice many of the theories circling successful approaches to e-learning. However, there are concerns on how these digital logs can gather information to be utilized effectively in an ethical manner. I am concerned about how thoroughly they will need to dive into student data without privacy concerns. Additionally, just because an individualized student demographic is inputted into the algorithm, I worry about overcoming any stereotypical notions and assess the student fairly. Personalized education was a major selling point but, unfortunately, there was no data supporting the same results for diversified in-person learning. 

Reference:

Arizmendi, C. J., Bernacki, M. L., Raković, M., Plumley, R. D., Urban, C. J., Panter, A. T., Greene, J. A., & Gates, K. M. (2022). Predicting student outcomes using digital logs of learning behaviors: Review, current standards, and suggestions for future work. Behavior Research Methods, 55(6), 3026–3054. https://doi.org/10.3758/s13428-022-01939-9Links to an external site. 

Data Graphs: Emerging e-learning tool

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Technological Innovations in Data Usefulness

This post is provided by guest blogger, Gerald Edwards Jr., graduate student University of St. Francis, MS Training and Development program

There is a massive amount of digital data available on any number of subjects. AI has pushed the boundaries of big data. Gleaning useful information from endless search parameters is an infinite challenge, especially in the e-learning setting. The use of AI and big data mining makes it nearly impossible for students, in the time frame of semester course work, to make meaningful connections between various subject matters. There is an emerging tool that is indispensable to students and researchers at all levels of education, but specifically in the e-learning setting.

An article published in the journal Heliyon, analyzes the available peer reviewed published research literature on the use of data graphs as an effective search technology which is highly adaptable and suited for the digital learning environment. The authors provide a systematic appraisal of user usefulness and successful research outcomes in the digital learning environment when searching topics using institutionally compiled data graphs. Data graphs allow seemingly independent information variables to interconnect and return more meaningful data to the user through AI and human compiled related data. This information is useful when students and institutions are interested in a comprehensive understanding of a specific topic. Data graphs have been developed in other areas but are emerging as a invaluable tool for higher education students and instructors participating in the e-learning environment.

Abu-Salih, B. & Alotaibi, S. (2024).  A systematic literature review of knowledge graph construction and application in education. Heliyon, (10)3. https://doi.org/10.1016/jheliyon.2024.e25383

e-learning

Effective Strategies for Instructional Design

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This post is provided by guest blogger, Hailey Kaddatz, graduate student at the University of St. Francis in Joliet, MS in Talent Development program.

Trif-Boia (2022) introduces techniques for concept design that increase success on training. We emphasize understanding the learner’s needs. Setting clear goals is essential. Finally, we suggest using a variety of teaching methods. The resource also stresses the importance of facilitating feedback and assessment in learning processes. In this way, teaching will not only be enjoyable to the learner, but right on target too.

Trif-Boia (2022) is on target, adopting recognized instructional design and teaching strategies. It is helpful not only for educators but also for those involved in the development of curriculum. In particular, it is important for those who want to adjust their teaching approaches. They want their learners to reach a better self.

Reference:
Trif-Boia, A. E. (2022, December). Instructional design in education. IJAEDU- International E-Journal of Advances in Education, (VIII) 24. http://ijaedu.ocerintjournals.org/en/download/article-file/2770833

ChatGPT Assessments: Rethinking Curriculum Design in an AI World

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This post is provided by guest blogger, Rachel Dobrich Ruffetti, a graduate student at the University of St. Francis in Joliet, working towards the Talent Development Certificate.

Artificial Intelligence (AI) tools like ChatGPT can potentially create personalized, interactive learning environments tailored to individual needs (Bennett & Abusalem, 2024). Since research remains limited, universities are reevaluating curriculum and assessment methods to integrate AI meaningfully. They are advocating for collaborative approaches that blend human intelligence with AI strengths. Key concerns include distinguishing between human and AI-generated work. Although source citation partially addresses this, redesigning assessment tasks can further mitigate risks. By leveraging Bloom’s Taxonomy, educators can emphasize higher-order thinking skills to reduce academic misconduct. Strategies, including monitoring assessment stages, incorporating self-reflection, and narrating presentations, can prove students’ original thoughts. Some universities are also exploring AI as research partners to enhance writing and language learning. While challenges exist, AI presents significant opportunities to enrich education. 

I recommend this article for instructors in higher education. It provides insights into how institutions are embracing AI rather than resisting it. Many concerns stem from a lack of research on AI’s role in higher education. However, this article offers a foundation for rethinking instruction and assessment. College should prepare students for the workforce, where AI will be integral. Rather than banning AI, institutions should engage students in meaningful projects that develop their ability to manage and implement AI responsibly. As the article emphasizes, thoughtful AI integration, with regulation and ethical considerations, can shift learning from rote memorization to critical thinking.

Reference
Bennett, L., & Abusalem, A. (2024). Artificial intelligence (AI) and its potential impact on the future of higher education. Athens Journal of Education, 11(3), 195–212. https://research-ebsco-com.ezproxy.stfrancis.edu/linkprocessor/plink?id=8838c1ff-5edb-3238-9627-e20d028c7409

Keywords: Artificial intelligence, ChatGPT, curriculum design, pedagogy, assessment.

Self-Regulated Learning in Online Education Using AI

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This post is provided by guest blogger, Erica LoBurgio, graduate student University of St. Francis, MS Training and Development Program.

In this article Supporting students’ self-regulated learning in online learning using artificial intelligence applications, authored by Yannis Vovides, Sara Sanchez-Alonso, Vasiliki Mitropoulou, and Gwendoline Nickmans, published in 2023 in the International Journal of Educational Technology in Higher Education, studies how the use of artificial intelligence can improve students in self-regulated learning during their online education. The article explains how utilizing artificial intelligence’s adaptive learning patterns, emotional assistance to students, and personalized responses help as tools and techniques required for the student’s needs.

The article delivers the importance of utilization of artificial intelligence for learning environments. There is a study that explains combined teacher support and constructed feedback, and the importance of it for the student. Specifically, learning outcomes in the online environments which help to assist the students in management of their learning processes more effectively.

Vovides, Y., Sanchez-Alonso, S., Mitropoulou, V., & Nickmans, G. (2023). Supporting students’ self-regulated learning in online learning using artificial intelligence applications. International Journal of Educational Technology in Higher Education. Retrieved from https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-023-00314-9

Mystery and History of Instructional Design

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This post is provided by guest blogger, Lynn Urban, graduate student at the University of St. Francis in Joliet, MS in Training and Development program.

Instructional Design (ID) in Higher Education often holds a mysterious existence for students and educators accustomed to traditional learning.  To uncover mystery, it’s helpful to look back and understand how ID became part of what we now know as “e-learning” teaching strategies.  Sharon O’Malley (2017) points to a time in military training where the practice emerged during World War II, “when the military assembled groups of psychologists and academics to create training and assessment materials for troops”. (O’Malley, 2017).  The author traces ID over decades, landing on the popularity of online courses and remote learning infiltration to higher education practices.  

I recommend this article for anyone interested in beginnings of instructional design, and the journey to gain acceptance in higher education. The article is from 2017, when there was still mystery surrounding the field. While written pre-pandemic (and much has developed since that time), the references to ID entering higher education are relevant.  Providing quotes and backgrounds of students and educators, readers can glimpse online technology being new and adaptation being slow.  Post-pandemic we see that ID has taken a larger space in education, but there is value to understanding history behind technology – to appreciate how far we have come.

Reference

O’Malley, S. (2017, August). Still a mystery. Inside Higher Ed.com. https://www.insidehighered.com/digital-learning/article/2017/08/02/what-do-instructional-designers-do

Effective Strategies for Effective Instructional Design

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This post is provided by guest blogger, Cody Stock, graduate student University of St. Francis, MS Training and Development program.

Effective instructional design is accomplished by using the proper teaching methods that align to the objectives of the course being developed. In a recent article, Steph Nagl, discusses instructional design methods that can be integrated into teaching to evoke the best outcomes for learners. The author discusses the following instructional design models: ADDIE model, Merril’s Principles of Instruction, Gagne’s Nine Events of Instructions, Bloom’s Taxonomy, and Backward Design/Understanding by Design. The author concludes that instructional design is most effective when the method that complements the course content and learning style of the students is utilized.

I recommend this article for anyone who works in instructional design and is looking to implement new strategies into their classroom. This article provides useful insights by asking each instructional design method and defining why it is effective when aligned properly to the objectives of the learning material. These instructional design methods are briefly described and are a great starting point for anyone looking to improve their online classroom environment.

Reference:

Nagl, S. (2023, May). Top 5 instructional design methods for effective teaching. Wake Forest University. https://sps.wfu.edu/articles/instructional-design-methods/

Ask not what AI is going to do to you, but what you are going to do with AI.

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This post is provided to you by genial and gracious guest blogger, Daniel Liestman, graduate student at the University of St. Francis in Joliet, MS in Training and Development program.

Before seeking to incorporate AI into training, one must first consider how to engage with AI in the design process itself.  AI can assist in conducting and assessing the learning needs analysis.   AI can create eLearning course outlines as well as build content. AI can also assist in writing introductions, crafting transitions, or crafting conclusions.  AI can also generate Level 1 and 2 activities and quizzes.  AI generated visuals can also be quite engaging!  Beyond design, AI can evaluate instructional design content.  AI to stays current with the latest trends in instructional design.

Most TD blogs on AI consider incorporating it into training. This blog realizes this is not the goal.  Numerous examples are offered which will make the most of free AI sites and tools.  This blog is iterative and provides important background information from previous entries to make for a more complete learning experience.  At the same time, none of these tools are fool proof.  Chat GPT3.5 is offered as a tool for summarizing a discussion and acknowledges some light proof-reading may be needed.  Light-proofing—HA!  Last time I tried it to summarize, I got nonsense reduced to gibberish. 

Proceed with caution.

Robertson, D. (2024). Improve your instructional design workflow with these 8 practical AI tool uses. Neovation. https://www.neovation.com/learn/87-8-practical-ai-tool-uses-for-your-instructional-design-workflow

(L)earning (M)ade (S)upportive Through LMS Data 

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This post is provided by guest blogger, Sejdije Fejza, graduate student at the University of St. Francis in Joliet, MS in Training and Development program.

In this article, researchers from the University of Queensland in Australia analyzed whether LMS data is useful in understanding learners and their needs. Video based learning management systems were assessed by monitoring how many times a student clicked on a video and the amount of time that video played. Exam results were also reviewed to determine whether LMS data can assist student learning. First, it revealed students’ preferences for learning. Additionally, students can reference back information or work on their own time. They also revealed that shorter videos and content receive the best results when instructing through this LMS. 

This source is helpful for organizations who are deciding what factors to assess in their LMS to support student learning. If individuals adopt this form of analysis to support learning, it is important that they carefully choose the best LMS features to assess. This will avoid limitations in data. For instance, it’s important to consider how engaged students are when watching a video. Therefore, the number of times clicked on a video may not necessarily correlate to whether the viewer is actively paying attention to the video to assist in their learning. 

Reference 

Maloney, S., Axelsen, M., Galligan, L., Turner, J., Redmond, P., Brown, A., Basson, M., & Lawrence, J. (2022). Using LMS log data to explore student engagement with coursework videos. Online Learning, 26(4), 399–423.

Assessing Micro-Learning in a Healthcare Education Environment

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This post is provided by guest blogger, Theresa Anderson, graduate student University of St. Francis, MS Training and Development program.

This article on the healthcare education micro-learning environment measure (HEMLEM) is a research article aimed at creating an evidence-based measurement tool for assessing clinical micro-learning environments across several healthcare professional student groups. The authors suggest there may be gaps between what healthcare professional students should be learning and what they learn. The authors took a mixed methods approach to create a micro-learning environment measure. They acknowledge that teaching quality, staff attitudes, and behaviors are critical for a good micro-learning environment. 

The article is helpful for anyone attempting to measure how micro-learning within the education of a healthcare professional environment affects students’ learning. The authors created a measurement tool through a step-wise approach: literature analyzing existing tools, generating new items through thematic analysis of student experiences, the Delphi process involving healthcare educators, piloting the prototype, and item reduction. The item reduction tool was a twelve-question survey of the students with differing healthcare professions. HEMLEM seems to be an efficient way to measure success or what needs to be improved in a micro-learning environment. 

References

Isba, R., Rousseva, C., Wolf, K., & Byrne-Davis, L. (2020). Development of a brief learning environment measure for use in healthcare professions education: The healthcare education micro learning environment measure (HEMLEM). BMC Medical Education, 20(110). https://doi.org/10.1186/s12909-020-01996-8