I am currently a Postdoctoral Fellow at the Human-Computer Interaction Institute, Carnegie Mellon University. I am working on a Bill and Melinda Gates Funded Research, "Prime Award",
related to "Exploring Cognitive-Metacognitive-Motivational Mutiplier Effects in Middle Year Math".
Under the mentorship of the Team Lead/PI: Prof. Ken Koedinger.
Exploring Cognitive-Metacognitive-Motivational Multiplier Effects in Learning Math
We are looking at personalising learning to students needs by exploring the opportunity to provide tutoring interventions
that is sensitive to their socio-emotion, motivation, and disposition toward math learning; especially the low-motivated
remedial students. In our current approach, we aim to develop tutor-training for Socio-Emotional Learning.
We exploit existing research for tutoring/teaching strategies that are sensitive to student’s socio-emotions and motivation;
and we develop tutor-training contents on these strategies. see more ...
Our goals in the development of the tutor-training content include: first, to provide the research-based information
succinctly and complete, such that tutors are aware of the strategies advanced in the research with less extraneous
Second, we exploit the predict-observe-explain inquiry method, to stimulate tutors’ authentic experience, reflection,
application, or adaptation in their practice. Third, integrate into existing design and resource in PL2,
a computer-based support for tutors to provide personalised math learning to a whole student.
The contributions that we envisage with our Computer-Based Socio-Emotion and motivation Sensitive tutor-training:
Targeting specific (and perhaps common) motivational issues that stem from discriminating socio-economic conditions of students.
Exploring existing research on subtle psychological and other interventions that seem worked in
experimental and/or practical learning contexts. This provides a baseline for tutors to reflect about
effective interventions in similar scenarios in practice.
Presenting the knowledge from these research findings, succinctly and completely.
Exploiting the Predict-Observe-Explain Inquiry to provide learning-by-doing experience,
bridging research findings with tutoring practice with little cognitive load on the practicing
tutors, and promoting tutoring practice that is sensitive to student’s socio-emotions and motivation.
Consequently, providing encompassing equal opportunity for achievement in math learning.
We have currently developed five tutor-training modules. These modules provide tutors with awareness of subtle
psychological and social strategies that can be applied to scaffold students’ self-efficacy in math learning, these includes:
Reacting to Errors: This module presents research findings the recommends ways that tutors can intervene when students
make mistakes or show misconceptions during learning activities, contributing to strengthening the student's self-efficacy
(Lepper, et al., 1993. pg. 80).
Framing Task Difficulty: This module provides insight and evidence on how tutors can frame task difficulty, when they introduce
a new learning task to their student, such that it promotes the students' sense of challenge, interest in the task, and #
confidence in their ability to be successful in the task (Lepper, et al., 1993. pg. 85).
Adapting Task Difficulty: Like Framing Task Difficulty, this module introduce research-based approach in which tutors can
modulate difficulty of tasks they assign to their students in a way that will strengthen the student's self-efficacy,
or belief in their ability to successfully complete a task (Margolis, & McCabe., 2006).
Recognizing growth attributions: Learning science and research suggests that student’s attribution to failure/success is an
indicator of their motivation to put effort in their learning and probability that they can persist when they encounter
impasses and challenges. This module assesses how much tutors can recognize their students' attribution process.
We provide reflection on how tutors can assess this psychological and affective state that influence their student’s growth-mindset (Lepper et., al., 2013).
Supporting growth attributions: Further on recognising growth attributions, this module provides subtle psychological approach
to support and teach students to attribute learning performance (success or failure) to factors that they can control,
such as effort and strategy. We reflect on how tutors stimulate a kind of vicarious experience in their students,
teaching them positive attributions to success/failure and supporting growth mindset (Dweck, 2015;
Lepper et., al., 2013).
In further research work, we will collect data of tutor responses and interaction with the computer-based training modules. We will analyse this
data to explore answers to the following questions:
- Is there evidence that the content of our tutor-training provides awareness and knowledge that is practical, reflective, applicable and/or adaptable?
- How effective is Predict-Observe-Explain Inquiry approach, in transferring the information conveyed in the training modules?
We will assess if tutor have authentic experience
with this training module, analysing data from tutors that undergo our training, for evidence of cognitive dissonance
In my PhD research work, under the tutilage and supervision of Prof. Judith Masthoff, I investigated Real-Time Assessment and Support for Online Collaborative Learning.
I explored computational framework to determine the level of collaboration when groups interact online to solve a learning task;
to support real-time monitoring of online group learners. We envision online learning systems with enriched socio-cognitive features
that provide learning experience that is as close as possible to the traditional classroom experience. see more ...
This research exploit was inspired by theories that reinforce socio-cognitive benefits of group learning. Such as, the Vygotsky's Zone of Proximal Development (ZPD),
the Piaget's social theory and the Cognitive Load Theory. Our framework of approach and study design was guided by our understanding of the Collaboration Management Life Cycle (CMLC), a framwork
for monitoring collaborative learning, proposed in (Soller et. al., (2005). ). My Phd research culminated
to contribute the following to the body of knowledge:
Definition and characterisation of collaboration indices from problem-solving interaction.
Collaborative learning environment abstraction (COLEA) - for generic application of CSCL research findings on group-learning.
Word-count/Gini-coefficient measure of symmetry (WC-GCMS): Mathematical metric model to quantitatively assess online collaboration.
Design framework for joint problem-solving (JPSD) chatroom, a web application to collect textual interaction data and experiment virtual group-learning discourse.
(Checkout JPSD Chatroom).
Insights into interaction data analysis.
Mirroring individuals’ efficacy in-groups to stimulate self-regulation to participate optimally.