Requirements for personalised blended (e)learning and training

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This document discusses requirements for personalized eLearning and training, including data and analytics to analyze student/trainee performance, feedback from teachers/instructors to support practice and transfer of skills from classroom to workplace, and encouraging student/trainee metacognition, reflection, and self-awareness through blended engagement with content and educational processes.

Education

Requirements for personalised (e)Learning and training
Data + Analytics
+ Analysis
Teacher/Instructor
/Coach
Practice Setting
Transfer
Theory to Practice
Student/Trainee
Metacognition
Reﬂection on and in Action
Self-awareness
Classroom Study Setting Workplace
Poh-Sun Goh

1st draft on 28 November 2017 @ 0810am
Blended
Engagement
Time Eﬀort Resources
Content
Educational and Training Process
Feedback
Feedback

The document discusses using learning analytics to track student engagement and application of knowledge from online educational content. It proposes separating content into modular posts including key words, quotes, links and notes for students to interact with. Students would then demonstrate applied learning by posting assignments, questions, and discussions showing how they are using and transferring the ideas to real-world practice. The goal is to promote meaningful learning and knowledge transfer from classroom concepts to other contexts.

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The document discusses using learning analytics to track student engagement with course content from initial attention and engagement through applying ideas in real world practice. It proposes separate modular blog posts that allow students to incorporate key ideas, quotes, and links while posting assignments, questions, and discussions. The goal is to make visible how students transform, integrate, and apply course content beyond the initial learning event.

Applied learning analytics for modular content (updated)

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The document discusses using learning analytics to track student engagement and application of knowledge from online educational content. It proposes breaking content into modular, bite-sized posts that students can interact with by including keywords, quotes, links and notes. Student interactions like posting questions, discussions and assignments would provide visibility into outcomes and how students are using and applying ideas in real world practice.

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This document discusses using learning analytics to encourage students to complete pre-class preparation assignments and materials. It proposes tracking various engagement metrics like views of online content, posts and responses on discussion platforms, and checkpoint submissions. This data could be analyzed as a whole for the cohort to see popular resources, and individually to understand each student's usage. The analytics would inform faculty actions like sending reminders, reorganizing content, and customizing learning paths. The goal is to align learning analytics with learning design to improve pedagogical approaches.

Applied learning analytics for modular content

Poh-Sun Goh

The document discusses using learning analytics to track student engagement with course material from initial attention and bookmarking, through note taking and highlighting, to reflection, commentary, and real-world application after class. It proposes separate, modular blog posts where students can publicly post quotes, notes, assignments, and discussion points that demonstrate how they are incorporating and applying key ideas from the material. The goal is to make visible how students are transforming, integrating, and applying the content in real world practice and promote transfer of learning.

Applied learning analytics for modular content

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The document discusses using learning analytics to track student attention, engagement, learning, and application of concepts from byte-sized blog posts on a modular online platform. Students would post responses incorporating key ideas, words, quotes, and links discussed in the lessons. Their notes and assignments would be visible to assess understanding and application of the concepts to real-world problems. The platform aims to support the learning process before, during, and after class.

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The document discusses various tools that can be used for applied learning analytics including blogger, Padlet, WhatsApp, and email. These tools can support activities like notes, reactions, assignments, comments, and discussions before, during, and after class. They also allow for temporal analysis of what content students found valuable, how it was accessed and used, cohort dynamics, how it was applied, and knowledge transfer to practice.

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