Tagged: W5A14

Week 5 – Activity 14: Evaluating an innovative pedagogy

Timing: 12 hours

Link to assignment: here.

Table saved on iCloud drive (week 5).



Part 1:

Pedagogy Cognitivism Behaviorism Constructivism Connectivism
Crossover learning


  • Blogging (esp. Tumblr/Pinterest)
“Learning in schools and colleges can be enriched by experiences from everyday life; informal learning can be deepened by adding questions and knowledge from the classroom.” (pg.3) “Increasingly, educators, policymakers and researchers view learning as a taking place across settings and connects in a ‘learning ecosystem’.” (pg. 11)

“Users of such tools provide records of their interactions and paths through information on the internet, so that one item or single collection can act as an entry point for a deeper exploration of a subject.” (pg12)

Learning though argumentation


  • Clickers
  • Polls
  • Cohere (mindmapping)
  • The Knowledge Forum
“Argumentation helps students attend to contrasting ideas, which can deepen their learning.” (pg.3)

“Asking questions that drive students to evaluate and improve their ideas.” (pg.14)

“Having students develop and use models to construct explanations.” (pg.14)
Incidental learning


  • Mobiles devices
  • Computer games (problem solving)
  • iSpot
“Incidental learning is unplanned or unintentional learning. It may occur while carrying out an activity that is seemingly unrelated to what is learned.” (pg.4) Creating meaning and links between this unplanned learning and other learning to build on knowledge.

“Schools are recognising that children can learn though play and discovery, making time for unstructured exploration.” (pg.17)

Context-based learning


  • Augmented reality.
  • Geo-learning (location aware devices) e.g. Aris.

(select for TMA)

“By interpreting new information in the context of where and when it occurs and relating it to what we already know, we come to understand its relevance and meaning. (pg. 4)

“Context is how we make sense of experience, by distinguishing between what is relevant and irrelevant. For example when reading a book, the meaning of each word and phrase is conveyed not only by its own characteristics, but also by its location in relation to other words or illustrations.” (pg. 20)

Computational thinking


  • Makerspaces
  • Programming
  • Scratch

(for TMA – lots to go on)

“Involves breaking large problems down into smaller ones (decomposition).” (pg.4)

Working together to:

“1. Examine a case and clarify terms.

2. Identify the problem.

3. Analyse the problem.

4. Draft an explanatory model.

5. Establish learning goals.

6. Work individually to collect information.

7. Apply and discuss additional information.” (pg. 24)

Learning based on problems for each learner to master and then progress. Viewed in a step by step manner. Building on steps.
Learning by doing science with remote labs


  • Scientific/lab equipment:
  • iLab Central, Go-Lab, OpenScience Laboratory
  • Computer-supported collaborative learning tools
Developing students understanding to undertake real world tasks. Can be linked to scaffolding.

Students “need considerable guidance in making sense of he data that results, relating the data back to their original driving questions, and deciding what to do next.” (pg. 27)

Embodied learning


  • Wearables
  • ‘Worked example’ videos for demonstration.
  • Table-top computers (better motor action)
“Gestures may also have a fundamental role in children’s development of mathematics and science, as they rotate shapes, organise things into categories, pour liquids, and move objects into alignment or set them in motion” (pg. 31).

Motor function learning:

“Using bodily actions when learning new content may result in deeper, longer-lasting memory traces and, in some aces, higher test scores and increases knowledge retention.” (pg.31)

“In embodied learning, the aim is that mind and body work together so that physical feedback and actions reinforce the learning process” (pg.5)
Adaptive teaching


  • Dashboards/User analytics

(select for TMA)

Suggestions for learning and teaching based on students past behaviour. The computer learning how the student learns. How each student learns differently with the same material:

“All learners are different. However, most educational presentations and materials are the same for all. This creates a learning problem, by putting a burden on the learner to figure out how to engage with the content” (pg.5)

Analytics of emotions


  • Analytics and adaptive technologies (based on student behaviour, can offer suggestions and determine learning proficiency)
  • Eye-tracking technology.
  • Mouse-tracking technology
“Automated methods of eye tracking and facial recognition can analyse how students learn, then respond differently to their emotional and cognitive states” (pg.5) Tracking students behaviour:

“Using analytics of emotions, institutions will be able to track which learning materials students are following, and whether they are distracts, guessing answers to quiz tests, or really engaged in learning.” (pg.35)

Could be used for training students to be in certain frames of mind to best approach learning.

Stealth assessment


  • Learner analytics (e.g. TAALES)
  • Gamification (e.g. Portal 2)

(closely related to Adaptive learning)

Based on students progress to provide instance response to learning. As such a student can learn from experience in a much faster manner.

“Stealth assessment can test hard-to-measure aspects of learning such as perseverance, creativity, and strategic thinking. It can also collect information about students’ learning states and processes without asking them to stop and take an examination.” (pg. 5)

“Stealth assessment extends adaptive teaching by making continual adjustments to a simulated environment, rather than selecting a path or exercise based on diagnosis of a learner’s knowledge and misconceptions”

(more real time adjustment rather than solely reflective) (pg. 38)


Part 2:

Selected innovation: Adaptive teaching. (potential impact: medium | 4 plus years)

Innovative technologies to assist in achieving this innovation:

  • Student analytics:
    • Pearson’s Mylab
    • Knewton
    • Smart Sparrow
    • CogBooks – provides an “adaptive course sequence [that] is neither a pre-set path, nor on based on test results, but it employs algorithms and machine learning methods that continuously tailer a learning sequence for each learner” pg. 34
    • Corego – Is memorisation software that “asks learners to set goals on the content they need to cover and the time available to study it, then designs a daily study plan.” pg. 34
    • Brainscape – It “adapts flashcards based on a learner’s confidence and available study time. Its model is informed by cognitive science research on how teaching methods should be sequenced, times and repeated in order to store knowledge reliably in long-term memory.” pg.34
    • Mathspring – Is a tutoring game that “sends encouraging messages to students and adapts the game difficulty depending on learner confidence, interest, frustration, or excitement.” pg. 34/35
  • Analytics of emotions (pg.9)
    • Wearable technology (“recent research into adaptive technologies is expanding beyond cognitive learner data by responding to learner’s moods and brain activity.” pg. 34
  • Learning from gaming to assist with incidental learning (pg.9)

What would this mean in practice? 

Key benefits: 

  • Provides an easy to understand overview of students abilities. This will help both students and teachers address “fans in their knowledge, and to accelerate their learning” based on more personalised learning tasks to enhance learner understanding. (pg. 34)
  • When dealing with students at different skill levels, or degrees of understanding, “adaptive teaching attempts to adjust to differences in background knowledge and experience, providing ways for learners to cope.” (pg.34)


  • From the data analysed by software, a better picture of students “brain activity, documents workload, boredom, off-task activity and engagement” can be monitored and compared to student output to tailer results (pg. 35).
    • This can result in students making better use of their time, and learning how to learn better (ref).
  • Teaching can become much more personalise and therefore fit the overall classes ability in a more efficient manner. Hence, the constructivist pedagogical view of individuals learning at their own pace can become a reality. This is exemplified in the following extract:
    • “Instead of being forced to do the parts that she considers boring, whenever the course reaches that point she can choose her own sequence in which to study the material. When she is tested at the end of each section, the more she is stuck on a problem the more hints she receives. Once she understands easier problems, she is given more  difficult ones. The computer suggests weaker areas she should review. At the end of the day, the teacher views the performance of each student and uses feedback from the system to find that a large group of students had trouble with certain questions, so plans either to re-teach those concepts in class the next day, or to split the students int smaller groups to discuss specific problems, as suggested by the software.” (pg.34)


  • Initial software development or implementation.
  • Staff training.
  • Adjustment of curricula to highlight key areas of learning – “The cost of developing adaptive teaching systems can be high if teaching materials must be produced to match the needs and interests of different learners.” (pg.35)
    • Adaptive learning “requires branched packets of content at several levels to provide multiple pedagogical approaches, confront common misconceptions, and offer a battery of hints that given learners the appropriate level of challenge and support.” (pg 35)
    • The design of these adaptive learning tutoring systems “can involved years of research into learners’ misconceptions” (pg.35). However, this will gradually be formed through a fast supply of research into this topic in more specific areas that will lesson the individual time requires per organisation to tailor their offering.


  • Is adaptive teaching, that is personalisation, incompatible with learning at scale (i.e. MOOCs?) pg.9.
  • Highly reliant of computational recommendations based algorithms.
    • It is unlikely for teaching to ever be fully replaced by algorithms. However, by having such advanced tools, it is possible that some teachers might rely too heavily on such technology and subsequently doubt their own professional judgement.

Overall recommendation:

There is an increasingly plethora of technology in this space, however as this grows, so does the need for understanding by professionals. It is not enough to fully trust a technology that claims to replace some, if not all, of teachers recommendations and the student’s approach to working through problems individually. It is recommended that both teachers and students should only use Adaptive technologies as a tool to enhance their teaching/learning, but not replace it and give it their full trust. Having said this, Adaptive technologies can provide valuable insights that could be overlooked, or not thought of. Using these insights, backed with personal judgement (a human touch), it is believed adaptive technologies will be most effective.