8.7.1 Providing materials with the right difficulty level
It is important to provide materials for a learner with the appropriate difficulty level. As I have discussed in the Engagement Boredom transition, 51.7% of the transition occurred when the learning material had low perceived value. 41.4% of the transition occurred when the learner had low control over the learning activity. When mental demands are too low, there is an insufficient challenge and a lack of intrinsic value, thus producing Boredom. Conversely, when demands exceed capabilities and cannot be met, it may be difficult to detect meaning in the activity, thus reducing its value. Therefore, Boredom can be experienced when learning materials are too easy (low mental demands) or too hard (high mental demand).
As I have discussed earlier, the learner’s knowledge base and learning capabilities could greatly impact her mental load as well as her overall affective experience while learning. Therefore, it helps the learning process to assess the student's learning capabilities and pre- course knowledge base before a lecture to provide materials accordingly. For example, the MOOC course could provide some optional introductory videos for learners lacking the necessary background and skills to take the course.
8.7.2 Distinguishing the types of boredom detected
Because Boredom is found to be associated with poor learning, researchers have explored the use of physiological signals, such as EEG signals and eye gaze data, to detect
Boredom/Disengagement in educational systems [42, 116]. However, none of these work detects
the types of Boredom, which can be triggered when one's mental demand is either high or low. Affect detection in a MOOC environment should distinguish the types of Boredom detected because different interventions should be deployed to handle these two types of
Boredom. To diminish the Boredom caused by easy activities, the system should introduce more
challenging content. For example, the system could skip the more basic explanatory video sections and jump to the important part. The system could also use interventions such as in-video quizzes to challenge learners and improve their engagement. On the other hand, for Boredom caused by high mental load, the system should increase the learner's understanding of the lecture. It could slow down the playback speed, rewind the video to go over the important parts, or provide adaptive reviews after the lecture.
8.7.3 Addressing learners’ confusion state
D’Mello’s affect dynamics model [33] posits the central role of Confusion and cognitive disequilibrium in deep learning. A major assertion that emerges from the model is that the
Engagement/Flow Confusion oscillations are beneficial to learning, and the system can
introduce Confusion to place learners in a state of cognitive disequilibrium in which they will have to stop, think, reason, and be active problem solvers. Unlike complex learning, MOOC learners are unlikely to engage in this productive deep thinking process because: 1) As opposed to deep learning activities, the MOOC activity mainly requires learners to memorize key phrases and facts in videos; 2) Due to the constant information flow of MOOC videos, learners are unlikely to spend much time thinking about the concepts/questions presented in the video. Therefore, is Confusion still a desirable state in MOOCs, and should the system even purposely induce Confusion?
My analysis of the transition from Confusion (cognitive disequilibrium) to Engagement (cognitive equilibrium) suggests that this transition relies more on the information provided by the video than on the effortful reasoning and problem-solving by the learner. Because this transition relies heavily on the content and flow of the video, which might vary greatly among different videos for different learners, I did not observe a significant Confusion Engagement transition in the MOOC learning session; rather I observed that a confused learner is most likely to stay confused. As Confusion accumulates, it is likely that the learner will eventually become frustrated when she can no longer follow the lecture. Apart from the persistence of Confusion and the Confusion Frustration transition, I also observed a possible fast transition from
Confusion to Boredom. As previous work suggests [49], some students “may not have enough skills and knowledge of math to experience much confusion other than an initial bewilderment
and quick escape.” If a student believes that the learning material exceeds her aptitude and her
effort will not help to master the material, she could quickly become bored and give up.
From the above discussion, it can be seen that Confusion in MOOC contexts is unlikely to elicit deep inquiry and learning gains; rather, it could easily lead to Disengagement and
Frustration if the challenge is too difficult for the learner. Therefore, rather than intentionally
causing Confusion, we should make the videos clear and easy to understand for general learners. Based on participants’ subjective feedback on what lead to confusion, I summarized a list of rules to help instructors produce better MOOC videos that eliminate unnecessary Confusion of learners:
• Avoiding making assertions or claims without any explanation or support.
• Adding necessary transitions between two topics (for example, explain why introducing the new topic and its relevance to the previous one).
• When presenting a concept involving much information (e.g., proving a mathematics equation), slow down the instructional pace. Avoiding putting information on a single slide all at once, clearly explaining the concept step by step.
• Using diagrams and demonstrations might help the learner understand a concept. • Using standard notations and symbols.
• Avoiding sloppy and unrecognizable handwriting.
8.7.4 Curiosity leading to better engagement
I found that the transition from Curiosity to Engagement occurred quite often. Thus, one effective way to improve student engagement is to make them feel curious about the upcoming content. I had discussed earlier how learners experienced Curiosity (0), the most common answer
was, “when the speaker posted a question and I was interested to hear what the answer was” [S4]. Therefore, in-video quizzes, which are adopted by many MOOC learning platforms (e.g., Coursera, Udacity), are indeed effective for improving learner engagement. Some participants also indicated that statements such as “we will address this issue later”, “we will now talk about
this problem in detail” pique their interests. Therefore, instructors could use these prompts now
and then to draw learners’ attention.