The learning MOOC perspective

By Ramón Talavera Franco

@ratafra

MIT and Stanford sponsored the “Learning with MOOCs: a practitioner’s workshop” on August 12-13, 2014 in Cambridge, MA. It was not clear for me who could attend to the workshops,  but I think that the event was “for invitation only”. Thankfully, MIT and Stanford transmitted some of the conferences live through webcast. Hence, I could have access to the keynote speaker conference.

It is interesting how the organizers divided the workshops. They used the modality of “tracks” (a 15 minute talk followed by 45 minutes facilitated discussion) to explore MOOC’s teaching, design, research, and technology. The workshop program was very interesting. Check the program here for more information.
The keynote speaker was Susan Singer, from the National Science Foundation (see here her bio). Singer’s presentation focused on undergraduates learning process, active learning, and the need to “reimagining research”.

Undergraduate learning process

Singer highlighted the need of understanding how undergraduates relate human thinking to a discipline, as well as their motivations to understand and apply findings to a discipline. In this respect, she explored some of the goals of discipline-based education research such as:

• Understanding how people learn concepts, practices and ways of thinking;
• Understanding the nature and development of expertise in a discipline;
• Identifying and measuring appropriate learning objectives and instructional approaches that help students advance toward those objectives; 
• Identifying approaches to make science and engineering education broad and inclusive.

Singer also discussed  how students benefit from blended courses. In this regards, she explored:

1. Students conceptual understanding and conceptual change:

• In all disciplines, undergraduate students have incorrect ideas and beliefs about fundamental concepts
• Students have particular difficulties with concepts that involve very large or very small temporal or spatial scales
• Several types of instructional strategies have been shown to promote conceptual change.

2. Problem solving and the use of representation:
  a. As novices in a domain, students are challenged by important aspects of the domain that can seem         easy or obvious to experts
  b. Students can be taught more expert-like problem-solving skills and strategies to improve their

       understanding of representations. To help students, she suggested:

• Social-mediated learning environments
• Open-ended problems
• Interventions to promote metacognition
• Scaffolding (steps and prompts to guide students)

3. Use of multiple representations

• Singer shared some examples on students’ outcomes regarding how novices and experts differ in the representation of a fact and how that representation affects reality.

4. Research of effective instruction

• Effective instruction includes a range of well-implemented research-based approaches
• Involving students actively in the learning process can enhance learning more effectively than lecturing
• The use of learning technology in itself does not improve learning outcomes (MOOCs are not magic)

Active learning

Singer explained how active learning has increased students’ performance in science, engineering and mathematics, and gave some examples on how active learning is changing traditional learning:

1. The flipped classroom:

• Putting lectures online and asking students to view in advance of class:
• Encountering the problem should proceed “telling”
• Research students who confront problems before the explanation appear better at transferring their learning to other context
• On sequencing learning can inform online courses

2. Blended learning. According to Singer, Community Colleges are showing success in using blended learning. For example:

• Welding simulations records students assessment based on speed, angle, and coordination
• Balancing sophisticated feedback from machine learning with face-to-face mentoring

Reimagining research

To reimagine research, Singer asked us to answer first why and what problems are we trying to solve, and how to use collaborative approaches for sharing big data. She emphasized the need of learning models and get data that inform our learning theory to design technology.

It was a great presentation and I learned a lot from it. Hopefully next year my experience in this topic can lead me to be invited to these workshops, or at least know about it on time so I can apply to attend. It is the place where I want to be!