Personalization vs. Teaching to the Average – Design Thinking in Adaptive Learning Experiences
By Gulinna A, Fort Hays State University
In a face-to-face class, the idea of “adaptive learning” occurs to an instructor naturally, as their teaching strategies evolve according to the target learner characteristics. By looking at students’ facial expressions and subtle gestures (e.g., nodding heads), an instructor could know about the majority of students’ level of knowledge. They then adjust teaching methods accordingly. But, in an online learning environment, an instructor could hardly know learner characteristics in the onset of a class. Synchronous and asynchronous communications could help bridge the gap, but it takes longer time than a face-to-face class. How do online instructors overcome not knowing their learners? How do they create the same engaging learning experiences for learners as they create in the face-to-face class? Adaptive online courses might be an approach to accommodate the needs for diverse learners, as this type of course uses algorithms to make the course tailor itself to individual leaners’ abilities and preferences. The 2017 NMC Horizon Report also asserted that adaptive learning strategies could be a retention solution in online and blended learning environments.
Creating a learner-centered environment is a common approach to designing online classes. This type of learning environment is designated to provide interactive and personalized activities for learners (Hannafin & Land, 1997). Many online course designers and developers focus on the content representation and course alignment (Hirumi, 2014; Maryland Online, 2014). However, the content is settled while learner characteristics are different from class to class. The optimization of content design and delivery should result from a holistic understanding of learners. In Gagne’s Nine Events of Instruction design process, knowing students’ level of knowledge is the prerequisite of effective instruction design (Gagne, Briggs, & Wager, 1992). Adaptive learning not only enables instructors to know about each learner’s level of knowledge but also provides personalized learning paths for learners.
Nursing is a discipline that requires lots of hand-on activities. In online learning environments, simulations and interactive videos help learners with practical knowledge inquiry prior to their real world problem-solving practices. The nursing graduate program is completely online at our University. Our instructional design team has collaborated with a nursing professor, i.e., the subject matter expert, to create a micro lesson about information inquiry of a client’s history of present illness (HPI). We have incorporated the theories on adult learning and situated learning to create an adaptive online lesson for the nursing graduate students at the University.
Malcolm Knowles (1980) defined andragogy to help adults learn. Problem-centered activities and practicality are the key principles in adult learning theory (TEAL, 2011). We created a series of problem-based activities to repeatedly test students’ knowledge acquisition and learning preferences to ensure the accuracy of personalized learning paths for them. According to Lave (1988), situated learning emphasized the development of authentic contexts to help learners with knowledge acquisition. In our micro lesson, the subject matter expert created the client persona as a 4-year-old boy with asthma, as well as the instructional materials. Our instructional design team then curated a series of learning activities about HPI information inquiry surrounding this topic.
To maximize the effectiveness of content representation, we have implemented Richard E. Mayer’s 12 multimedia learning principles to accommodate different learners’ learning preferences and reduce their cognitive load of information processing (Mayer, 2005). We created this micro lesson with small segments, and always displayed on-screen texts with relevant graphics to help learners process information (see Figure 1). Moreover, we also used four-component instructional design system (4C/ID-model) to guide the sequence of learning activities, as this model is an effective instructional design model for blueprinting complex learning (van Merriënboer, Clark, & de Croock, 2002).
Figure 1: A Multimedia Learning Example of On-screen Texts with Graphics
The instructional design models help with content representation and structure, but not so much on creating personalized learning experiences for learners. Unlike traditional instructional design models, design methodologies used in the field of interactive design are more flexible and user-oriented. Design thinking process is a user-centered design methodology for solving problems and creating innovation. According to the process model proposed by the Hasso-Plattner Institute of Design at Stanford, the design thinking process involves five stages (See Figure 2). We followed these five stages to design the prototype of our adaptive online lesson:
Figure 2: The 5 Stages of the Design Thinking Process
1. Empathize. This step helped the design team understand the target users, e.g., physical and emotional needs, thinking patterns, and interested areas. In this step, we created the persona of different target learner types. Here are several strategies to implement at this stage:
- Interviews with students
- Focus groups with students
- Talk to subject matter experts
2. Define. Once the design team identified the target learners in Stage 1, we started addressing the challenges that target learners might encounter. As a problem-solving method, this stage helps identify the potential problems and challenges in the lesson design. The design team described potential challenges for each learner type as identified in Stage 1. A useful strategy for this stage is to identify the learning objectives of the lesson, and then go over each objective to map the possible challenges from different learner types.
3. Ideate. This stage is to brainstorm the possible solutions for the challenges and problems identified in Stage 2. We used Google Doc to collect ideas. The ideas and solutions in this stage should be as wild and creative as possible. One useful strategy for this stage is to categorize the ideas into “unexpected”, “rational”, and “creative”, and then carry on with the most favorable ideas as voted by the majority of the design team to Stage 4.
4. Prototype. Based upon the ideas that selected from Stage 3, this stage is to create the storyboard of the possible solution. This stage is about turning the ideas into possible design solutions but not settled or flawless plans. The design team need to be careful about the depth of each prototype. Several strategies could be used at this stage:
- An outline/storyboard template for the lesson
- Interface mock-ups
5. Test. This stage is to gather the feedback from learners about the prototypes. Learners will interact with the prototype, and report the working and non-working sections in the lesson. This stage is to refine the design of problem solution. One useful strategy for this stage is the design team could create a series of PowerPoint slides with detailed design mechanics, and ask for reviewers’ comments.
Compared to “teaching the average” in a face-to-face class, an instructor will be able to craft personalized learning experiences for every learner in the online learning environment. Although many learning management systems (LMS) have adaptability functions, the overall interface design is too static to create pleasant learning experiences or interactions for learners. Adaptive learning platforms create “one-stop-shop” learning experiences for learners to study personalized learning content that fits their level of knowledge. Technically speaking, adaptive online lessons can be easily embedded to LMS for seamlessly learning and grading.
We used Smart Sparrow to create this micro lesson. Smart Sparrow is a learning design platform that supports adaptive e-learning course creation. The authoring interface of Smart Sparrow looks similar to Microsoft PowerPoint, which has the tools bar on the top and a menu bar to the left. Our instructional design team used “trap states” to create just-in-time information and appropriate pathways for learners. Trap states can be set upon learners’ performance in the current activity or previous activities (see Figure 3).
Figure 3: A Screenshot of the Built-in Programming of Trap States in Smart Sparrow
Smart Sparrow has built-in programming for trap states and a variety of activity types (see Figure 4). We created each activity on one screen, and set them up as individual sections or layers. In this lesson, we have implemented several activity types to optimize content representation and user experiences, e.g., pop-ups, multiple choice questions, drag-and-drop, and drop-down menu. Each activity plays a role in knowledge acquisition reinforcement.
Figure 4: Available Tools in Smart Sparrow
We used “pop-ups” to present new content to learners. This technique helps reduce learners’ cognitive load of information processing. When learners mouse over each picture on a screen, its related information will reveal. We used “timer” to control the minimum time learners need to spend on a screen. If a learner skipped the content to advance to the next activity, a warning message will show up and remind them to read the content prior to access the next screen. After reading the content, learners will complete at least one assessment to demonstrate their reading comprehension. The assessment could be a multiple choice question, drag-and-drop acidity, or other interactive activities.
Learners will receive immediate feedback about their responses and performances (see Figure 5). For example, if a learner’s answer is correct, a congratulation message will show up with information that reinforces learner’s knowledge. If a learner provided a wrong answer, they will get a message with hints and tips for the correct answer. If a learner failed the assessments for three times, they will be sent to a remedial lesson for scaffolding instruction. Sometimes learners could advance to the next activity if the tasks they failed were low-stakes assessments.
Figure 5: An Activity Example of Information Inquiry Skills in Nursing in Smart Sparrow
This lesson is the first micro lesson we have created for the Nursing Department at the University. After testing the prototypes of this lesson, we will create a series of micro lessons to supplement the nursing curriculum. These exemplary lessons will be used as template lessons for future faculty members who are interested in creating adaptive online lessons in their courses. Smart Sparrow also provides an analytics interface for course developers to track learner performances (see Figure 6). Based upon the increasing number of student performance data, we will modify the lessons to increase their effectiveness.
Figure 6: Analytics Interface of Smart Sparrow
Gagné, R. M., Briggs, L. J., & Wager, W. W. (1992). Principles of instructional design (4th ed.). Forth Worth, TX: Harcourt Brace Jovanovich College Publishers.
Hannafin, M. J., & Land, S. M. (1997). The foundations and assumptions of technology-enhanced student-centered learning environments. Instructional science, 25(3), 167-202.
Hirumi, A. (Ed.). (2014). Grounded designs for online and hybrid learning: Trends and technologies. Washington, DC: International Society for Technology in Education
Knowles, M. (1980). The modern practice of adult education: Andragogy versus pedagogy. Englewood Cliffs, NJ: Cambridge Adult Education.
Lave, J. (1988). Cognition in Practice: Mind, mathematics, and culture in everyday life. Cambridge, UK: Cambridge University Press.
Maryland Online, Inc. (2014). Quality matters higher education rubric. (5th Ed.). Annapolis, MD: Author.
Mayer, R. E. (Ed.). (2005). The Cambridge handbook of multimedia learning. Cambridge, UK: Cambridge University Press.
NMC Horizon Report. (2017). Higher education edition. Austin, Texas: The New Media Consortium.
TEAL Center Staff. (2011). TEAL center fact sheet No. 11: Adult learning theories. Retrieved from https://lincs.ed.gov/sites/default/files/11_%20TEAL_Adult_Learning_Theory.pdf
van Merriënboer, J. J., Clark, R. E., & De Croock, M. B. (2002). Blueprints for complex learning: The 4C/ID-model. Educational technology research and development, 50(2), 39-61.
About the Author
Dr. Gulinna A is an instructional designer in the department of Teaching Innovation and Learning Technologies at Fort Hays State University. She graduated from the Master’s Program in Educational Technology at the University of Kansas in 2012, and achieved the doctorate degree in Educational Leadership and Policy Studies with a focus in Educational Technology at the University of Kansas in 2016.
Dr. A helps university faculty members with blended and online course development as well as consultation for effective teaching and learning solutions. She also offers workshops on emerging technologies and instructional design. Her research interests are gamification in education and influential factors that affect student perceptions of active learning.
The author may be reached at firstname.lastname@example.org.
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