The roadmap to emotionally accessible MOOCs
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15 May 2017
The evolution of open education on the internet is enabling thousands of people around the world to follow different educational initiatives. A basic characteristic of MOOCs, independently of its type, is the high degree of interactivity that facilitates and reinforces the bidirectional communication between the learners and the mediators. Therefore, the teachers and moderators inside the MOOCs act like community managers. As the learning is learner-centered, it requires a greater commitment from the learner into self-learning, deep research aptitude and analysis, reflexive capacity along with a high component of personal autonomy. An effective open e-learning environment should consider each learner’s abilities, learning goals, where learning takes place, and which specific devices the learner uses; when it comes to using screen reader technologies students already make personalized choices in terms of which synthetic voice they use to hear to course material. The main driving force behind this investigation is to determine if the emotional expression of a synthetic voice influences the performance of learners. This presentation will outline the results of measuring the emotional expression in three MOOCs when samples of the course text are read through text-to-speech. The text was inspected by raters for emotional content. There is likely reason to begin examining what implication are when the emotion prediction of the text-to-speech disagrees with the human raters as well as pursuing more intentional emotional expression based on emotional detection in text. There are implications as the accessibility technologies could potentially be undermining the intent of course designer’s deliberate decisions to use emotional expression in text. The effects on learning are as yet unknown, but there is a potential for dynamic personalization with synthetic voices to provide an accessible alternative that could potentially be superior to the single voice of an instructor.