This issue of LSW is focused on self-directed learning and learner engagement. The questions we’re focusing on this week are:
- How do instructional design factors impact independent online learning?
- In a self-directed learning context, what are learners' intentions for further engagement?
- How does emotional intelligence relate to learner engagement?
Considering our past year in remote environments, I'm excited to present articles about driving learning engagement in online learning. Let’s get to it!
Instructional Design & Self-Directed Learning
A study published last year aimed to assess how instructional design factors related to independent online learning. The researchers use POEE Scaffolding, which includes elements that assist learners in going through the process of Predicting, Observing, Explaining, and Evaluating a situation. While POE (POEE minus “evaluate”) has been shown to promote conceptual understanding and the construction of scientific knowledge, this study expanded previous research by adding the Evaluate component (Hsu, Tsai, & Liang, 2011). Further, this study is specifically set apart from others in that the evaluation is aimed at providing feedback for students in lieu of synchronous instructor feedback.
In an independent online learning environment, learners need to have high levels of self-regulation. That is, they should be able to understand their current knowledge, have high self-efficacy (believe they can do it!), and efficiently use learning strategies. The researchers were interested in how students would benefit from this self-directed learning in each of the POEE stages.
So, what did they find? The ‘predict phase’ did create cognitive conflict with students, i.e., they were made aware of the difference between their existing knowledge and the information provided by the module. In the ‘observe’ phase, learners' visualization skills were improved with dynamic representations. However, learners did express a need for more instructional guidance within the ‘observe’ phase. The ‘explain’ phase illustrated a positive relationship with inquiry learning. In the ‘explain’ phase, students were required to rate their confidence levels in the explanations they provided. The results illustrated that while rating, students became more mindful of their answers by checking. This shows that learners utilized metacognitive skills. In the last phase, ‘evaluate,’ the researchers found that students receiving feedback on incorrect answers would re-visit and explore the simulations in that module. The study concluded that feedback in this phase should be very specific, which will minimize cognitive load (see Issue 6 of LSW).
Overall, Mamun and colleagues (2020) put forward an excellent expansion on the POE model. They found that multimodal scaffolding will help accelerate learning within a STEM context. In addition to the POEE scaffolding, they recommend using micro-scripted scaffolding. Specifically, this scaffolding should include detailed instructional guidance, multiple external representations, and inquiry questions throughout. Asking learners to reflect and elaborate on a topic is more effective than repetition (Goldstein, 2019). This study is a great step toward designing online instruction that mitigates the need for immediate instructor support.
Key Finding: For independent online learning environments, instructional designers should consider incorporating inquiry questions, instructional guidance, and detailed feedback as effective ways to mitigate the need for immediate instructor support.
Read More ($): Mamun, M. A. A., Lawrie, G., & Wright, T. (2020). Instructional design of scaffolded online learning modules for self-directed learning and inquiry-based learning environments. Computers & Education, 144.
"Learners are likely to utilize self-directed learning strategies in the learning processes if they feel connected to or engaged with the content.” - Kim et al., 2021
Course Design, Self-Directed Learning, & Intentions for Further Learning
Continuing our trend of self-directed learning, we’re surfing over to MOOCs (Massive Open Online Courses). A study published this year in Computers & Education takes into account course design factors, self-directed learning, and intentions for further learning. I was particularly intrigued by the inclusion of intentions for further learning, as this is a variable largely untouched by the literature to date but has huge implications. Understanding if learners intend to continue with self paced courses is the difference between “how do I make this course successful?” and “how do I make my program of courses successful?” Let’s dive right in!
Kim et al. (2021) analyzed survey responses from 664 learners on an MOOC focused on our favorite subject: “Learning How To Learn!” While past work shows that course design is crucial for student success and usage of MOOCs (Fianu et al., 2018), this study adds to the topic by including self-directed learning and intention for further learning. As mentioned above, self-directed learning skills are crucial for success in digital learning environments. Learners must set and achieve appropriate learning goals. Research also shows that high self-regulated learners are more likely to follow the course format, as well as have a deeper comprehension of the content (Maldonado-Mahauad et al., 2018).
The results from this survey reaffirmed earlier findings regarding course design. They found that course structure was integral to self-directed learning in MOOCs. Learners that were engaged with the materials were more likely to use self-directed learning strategies. Beyond that, learner self-directed learning was positively related to their intent for further learning. Meaning, if a learner is engaging in self-directed learning they are more likely to continue with the MOOC format (i.e., take more courses with your program). This can be interpreted in a few ways, but the authors suggest this means self-directed learners perceive this course format as a “manageable learning resource.” This adds to the literature since we now know that course design alone does not account for intent to continue; self-directed learning mediates the relationship. When looking at the practical applications from this study, the authors suggest that MOOC designers implement ways to encourage learners to engage with the material and relate to the course content.
Key Finding: When digital learners engage with materials in MOOCs, they are more likely to utilize self-directed learning skills. In turn, this may make learners more likely to pursue further learning within the MOOC context.
Read More ($): Kim, D., Jung, E., Yoon, M., Chang, Y., Park, S., Kim, D., Demir, F. (2021). Exploring the structural relationships between course design factors, learner commitment, self-directed learning, and intentions for further learning in a self-paced MOOC. Computers & Education, 166.
Emotional Intelligence & Learning Engagement
Our last article for this week is all about driving learner engagement. While past research has shown that emotional intelligence is important for managing the demands of academic settings, little work has assessed how emotional intelligence might relate to academic engagement. We also know that students with higher levels of cognitive engagement are generally more likely to use self-regulated learning strategies (see Distributed Practice in LSW Issue 43). Moreover, academic buoyancy, which speaks to a learner’s ability to cope with stressors related to learning situations, has yet to be added to this line of research (Martin & Marsh, 2008). Considering emotional intelligence is related to an ability to self-regulate, and academic buoyancy deals with coping - the authors of the study discussed here wanted to know whether the relationship between emotional intelligence and students’ engagement is mediated by academic buoyancy.
To assess the relationship between these factors, survey responses from 253 undergraduate and graduate students in a small public university were analyzed. The researchers took data regarding emotional intelligence, behavioral and emotional engagement, behavioral and emotional disaffection (i.e., the opposite of engagement), and academic buoyancy. Their findings echoed past work regarding the relationship between emotional intelligence and emotional engagement; students with higher emotional intelligence were more likely to illustrate emotions such as interest and enjoyment. However, their results also showed that emotional intelligence had a direct influence on behavioral engagement, which is a novel finding! This means that learners high in emotional intelligence were more likely to show increased effort to their educational tasks, and less likely to experience behaviors such as withdrawal or being unprepared for the course. Regarding academic buoyancy, the results showed that the relationship between emotional intelligence and engagement was partially mediated by academic buoyancy. In essence, emotional intelligence might enable learners to develop effective coping strategies for the academic setting (academic buoyancy).
The results from Thomas and Allen’s (2021) work has interesting implications for instructors. The authors advocate for educational content that helps learners to foster emotional intelligence. Even in the adult learning literature, we see that learners high in emotional intelligence are more likely to be both emotionally and behaviorally engaged. Thus, providing an “intervention” of sorts may promote higher academic achievement.
Key Finding: Learners that are higher in emotional intelligence may develop coping skills that enable them to be “academically buoyant,” which seems to promote higher levels of academic engagement.
Read More ($): Thomas, C. L. & Allen, K. (2021). Driving engagement: investigating the influence of emotional intelligence and academic buoyancy on student engagement. Journal of Further and Higher Education, 45, 107-119.
Educating Tomorrow's Workforce
Thanks to reader Steve N. for letting us know about this upcoming virtual event happening July 27-28; learn more and register at https://www.massbridgesummit.org/.
The pandemic brought home an urgent need: the best-educated are prospering but we are leaving too many behind. Community Colleges are a rich resource for educating higher skilled workers that industry is now demanding. However, schools, working with employers and policymakers, must do more to bridge the gap between education and employment.
This conference will host thought leaders from all parts of the education-workforce equation to discuss how we can expand and create new training opportunities that prepare students for quality jobs. Building on new models discussed in a new MIT study (MassBridge Advanced Manufacturing Education Benchmark Report) we will hear from educators, policymakers, industry, and students about how to bridge the gap. Advanced manufacturing can be one source for well-paying, highly skilled jobs, but our discussions will extend to other sectors, as well – health, financial, information technology. Please join us and help to set a new agenda for bridging the education / workforce gap.
The conference is hosted by MIT Open Learning and MassBridge, a state-wide effort to build new education models for advanced skills.
Pets of Learning Science Weekly
Reader Steve N. also shared with us a picture of his Tonkinese buddy Guinness and says, "He’s super spoiled and a bit of a diva but we love him." Thanks for sharing, Steve, and thanks for being a loyal LSW supporter! 🥳
Send me (hi, I'm Julia) your pet pics at email@example.com.
Wondering why we’re including animal photos in a learning science newsletter? It may seem weird, we admit. But we’re banking on the baby schema effect and the “power of Kawaii.” So, send us your cute pet pics -- you’re helping us all learn better!
The LSW Crew
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