The scientific research behind learning with Kwizie

Kwizie is fun, but it’s also extremely effective. Robust learning science principles guided us in the unique design of Kwizie with the four major pillars presented below.

Human-Centered AI

Human-Centered AI (HCAI) is defined by the Stanford University HCAI Lab as “AI research inspired by the depth and diversity of human minds, concerned at each step with its ethical impact on people and society, and intended to augment human capabilities rather than replace them” [1]. Research on HCAI design has been shown to be far more effective at aiding the learning process than completely automated AI approaches [2]

Kwizie's AI is designed to learn from a diversity of human interaction. For example, instead of traditionally taking multiple-choice quizzes only in a passive way by selected the correct answer, quiz participants are empowered to appeal the result, rate, and comment on each question. This unique design enables powerful reflective learning for both the quiz master AND quiz participants. As a bonus, it also helps to continuously improve the AI to create better questions and answers.

Learn Socially  – Social Constructivism


‍A term called social constructivism is commonly used in academia that investigates the role others play inour understanding about the world [3]. Studies have shown time and time again across contexts that learning can be much more effective and engaging when interaction with others is enabled [4]. In Kwizie, you can easily take quizzes together synchronously (like Kahoot) to learn at the same time with others or individual asynchronously (like Quizlet). Easily share a unique quiz pin code to get others to join. You can also share copies of quizzes with each other to reuse and remix for different uses.

Student-Driven Peer Assessment

Kwizie is founded in Finland where there is a firm value placed on student-centred learning [5]. We strongly believe in a pedagogical approach where the student is the driver of the learning process. We also believe that assessment should mostly used to promote a growth mindset and not as an instrument used to categorise students into high and low achievers [6]. In this spirit, empowering students to become the ‘teacher’ or the ‘assessor’ by making and sharing assessments for their peers is a powerful way to promote metacognitive abilities like critical thinking [7]. By reflecting and critiquing about the effective questions and answers on a particular topic enables powerful opportunities to develop these 21st century skills in Kwizie.

Regular Retrieval Practice, Pretesting, & Formative Assessment

Cognitive scientists have agreed for decades that our brains are very good at filtering out information it perceives as not useful [8]. If learners passively view videos, lectures, or read articles, they often only remember a small fraction a few hours later [9]. This is why it is essential learners interact with the content they are trying to understand to make it stick [10]. Getting regular and timely feedback on retention and understanding also optimises learning further [11]. However, making interactive resources and marking manually with current available tools is very time consuming that tend to get out of date easily.  

Kwizie maximises the time efficiency to create interactive learning experiences and automate marking with the help of cutting-edge AI. This competitive advantage makes retrieval practice and pretesting far more regular which optimises possibilities for formative assessment [12]. For example, taking a quick pretest with Kwizie before you even start studying is a powerful way to learn better, even if you get every question wrong [13]. Then practice the terms you missed.

Check out Kwizie's main use cases

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References

[1] What Is Human-Centered Artificial Intelligence?, Stanford University HCAI Lab 2020, https://www.youtube.com/watch?v=4W2kXBBFDw4.

[2] Jiahong Su, Yuchun Zhong, and Davy Tsz Kit Ng, ‘A Meta-Review of Literature on Educational Approaches for Teaching AI at the K-12 Levels in the Asia-Pacific Region’, Computers and Education: Artificial Intelligence 3 (2022): 100065, https://doi.org/10.1016/j.caeai.2022.100065; Stephen J.H. Yang et al., ‘Human-Centered Artificial Intelligence in Education: Seeing the Invisible through the Visible’, Computers and Education: Artificial Intelligence 2 (2021): 100008, https://doi.org/10.1016/j.caeai.2021.100008.

[3] A. Sullivan Palincsar, ‘SOCIAL CONSTRUCTIVIST PERSPECTIVES ON TEACHING AND LEARNING’, Annual Review of Psychology 49, no. 1 (February 1998): 345–75, https://doi.org/10.1146/annurev.psych.49.1.345.

[4] Palincsar; Çetin Semerci and Veli Batdi, ‘A Meta-Analysis of Constructivist Learning Approach on Learners’ Academic Achievements, Retention and Attitudes’, Journal of Education and TrainingStudies 3, no. 2 (25 February 2015): 171–80, https://doi.org/10.11114/jets.v3i2.644.

[5] Pasi Sahlberg, Finnish Lessons 3.0: What Can the World Learn from Educational Change in Finland?, Third edition (New York: Teachers College Press, Columbia University, 2021).

[6] Carol S. Dweck, Mindset: The New Psychology of Success, Ballantine Books trade pbk. ed (New York: Ballantine Books, 2008).

[7] Kit S. Double, Joshua A. McGrane, and Therese N. Hopfenbeck, ‘The Impact of Peer Assessment on Academic Performance: A Meta-Analysis of Control Group Studies’, Educational Psychology Review 32, no. 2 (June 2020): 481–509, https://doi.org/10.1007/s10648-019-09510-3.

[8] Jordana Cepelewicz, ‘To Pay Attention, the Brain Uses Filters, Not a Spotlight’, Quanta Magazine, 24 September 2019, https://www.quantamagazine.org/to-pay-attention-the-brain-uses-filters-not-a-spotlight-20190924/.

[9] Sabine Heim and Andreas Keil, ‘Too Much Information, Too Little Time: How the Brain Separates Important from Unimportant Things inOur Fast-Paced Media World’, Frontiers for Young Minds 5 (1 June 2017): 23, https://doi.org/10.3389/frym.2017.00023; Henry L. Roediger and Andrew C.Butler, ‘The Critical Role of Retrieval Practice in Long-Term Retention’, Trends in Cognitive Sciences 15, no. 1 (January 2011): 20–27, https://doi.org/10.1016/j.tics.2010.09.003.

[10] Marc Augustin, ‘How to Learn Effectively in Medical School: Test Yourself, Learn Actively, and Repeat in Intervals’, The Yale Journal of Biology and Medicine 87, no. 2 (6 June 2014): 207–12.

[11] Leopold Bayerlein, ‘Students’ Feedback Preferences: How Do Students React to Timely and Automatically Generated Assessment Feedback?’, Assessment & Evaluation in Higher Education 39, no. 8 (17 November 2014): 916–31, https://doi.org/10.1080/02602938.2013.870531.

[12] Lindsey E. Richland, Nate Kornell, and Liche Sean Kao,‘The Pretesting Effect: Do Unsuccessful Retrieval Attempts Enhance Learning?’, Journal of Experimental Psychology: Applied 15, no. 3 (2009): 243–57, https://doi.org/10.1037/a0016496; Roediger and Butler, ‘The Critical Role ofRetrieval Practice in Long-Term Retention’.

[13] Alice Latimier et al., ‘Does Pre-Testing Promote Better Retention thanPost-Testing?’, Npj Science of Learning 4, no. 1 (December 2019): 15, https://doi.org/10.1038/s41539-019-0053-1.