PERLS recommends content based on the learner’s goals, readiness, availability, location, deadlines, and other contextual factors. Each card includes a text sell point explaining why PERLS made the recommendation.
Each phase of the Self Regulated Learning model has associated goals, content types useful for advancing those goals, typical failure modes, and strategies useful for preventing or remedying failure. Learners can start and end at any point, following individualized paths.
Learners decide their level of commitment to studying a topic, and PERLS generates a learning plan. For instance, selecting Explore will lead PERLS to recommend additional content on the topic, preferring items that are interesting but not very challenging. If Study is selected, PERLS will recommend content representing a logical progression of content, from introductory material to challenging lessons.
Action cards encourage learners to engage in reflection, goal-setting, planning and other metacognitive, motivational, and volitional self-learning strategies. PERLS tracks progress to better understand learner interests, and can encourage to learners to step up and set a learning goal for a topic in which a pattern of interest emerges.
Learning content is spread among may sources and buried in diverse specialized apps. Learners will not know where to look. PERLS can recommend content from external repositories, feeds, and siloed learning application content such as lessons created in the PALMs system. Third party content can be imported into PERLS or registered but hosted remotely with user progress data via PERLS APIs.
For more information about PERLS, email Michael Freed.