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What is machine learning?
What is machine learning?
Machine learning takes advantage of computers' processing power to teach programs to analyze massive amounts of data in order to make connections, see patterns, and improve over time. Instead of being explicitly programmed for every scenario, machine learning enables the program to make decisions based on the examples and data sets it has been given, and to change those decisions as it receives additional data over time.
Machine learning takes advantage of computers' processing power to teach programs to analyze massive amounts of data in order to make connections, see patterns, and improve over time. Instead of being explicitly programmed for every scenario, machine learning enables the program to make decisions based on the examples and data sets it has been given, and to change those decisions as it receives additional data over time.
Examples of machine learning in the classroom
Examples of machine learning in the classroom
See his research paper below that informs this talk!
See his research paper below that informs this talk!
Examples of Companies Using Machine Learning
Examples of Companies Using Machine Learning
Turnitin
Turnitin
Uses machine learning to check papers for plagiarism. It compares student-submitted works to works available online using both identical language searching and paraphrased language searching.
Querium
Querium
Uses machine learning to provide immediate feedback and tutoring to students in math and science. Uses natural language models to help students understand mistakes.
DreamBox Learning
DreamBox Learning
Uses machine learning to analyze student performance on math activities, including time spent on each activity, to tailor future learning to their needs.
Duolingo
Duolingo
Uses machine learning to recognize speech and give feedback on pronounciation, tailor lessons to users based on past lessons, and translate between languages.
Alignment with distance learning principles
Alignment with distance learning principles
Accessibility
Accessibility
Language models and AI tools can caption live speakers and posted videos to help make content more accessible.
Machine learning can auto-translate live speakers, videos, and articles for distance learners who speak other languages than the source material. This can also help facilitate discussions between students and with the teachers.
Speech recognition tools can help students use text-to-speech tools to help with written assignments and research.
Machine learning and AI tools can serve as personal tutors for students to help bridge the access gap between students of different SESs.
engagement & motivation
engagement & motivation
Machine learning programs can learn students' habits and preferences in order to design more engaging lessons and activities for them.
By analyzing students' knowledge and past mistakes, machine learning can target students' Zones of Proximal Development to keep them from getting frustrated by content that is too advanced or too easy for them.
Natural language models can interact with students as a virtual tutor, like in Khanmigo, to increase engagement with topics like coding, writing, and history, by giving them 1:1 attention and providing an interactive learning experience.
customized and personalized learning
customized and personalized learning
Machine learning is able to learn the preferences of students and the things they engage with most in order to provide them an experience that is most engaging for them.
Programs are able to predict students' needs and supply them with suggested resources, methods, and paths to extend their learning.
Machine learning is able to help teachers plan engaging activities for their students, as well as be the engaging activity for students as it adapts to their level. Machine learning can also adapt to the needs of the teacher as it finds out what kinds of activities they use and how to assist in lesson planning to meet the needs of the class.
continuous assessment
continuous assessment
Machine learning can analyze student performance each time a student interacts with the program. As the machine learns student habits and proficiencies, it can also generate a report for teachers.
Machine learning has been used to predict student attrition and dropout rates in time for teachers to intervene and prevent them.
Machine learning is able to recognize speech and images in order to assess multimodal projects and submissions.
Some applications have used machine learning to evaluate the originality of sources and student work, which speeds up the process of assessment and gives teachers time to give more qualitative feedback.
Benefits & challenges
Benefits & challenges
Benefits
Benefits
Challenges & Limitations
Challenges & Limitations
Adaptability
Adaptability
Once a tool has been trained for its function, it is able to adapt to many different users and inputs in order to customize itself to the user. It is able to remember the users' preferences over time to create a customized experience.
Time to Learn
Time to Learn
Unless the model has already been trained on data sets, getting a machine enough data to know users well enough to be useful is a time-consuming task.
Time Saver
Time Saver
Machine learning tools that can help teachers and students target the content that needs to be learned most and teach it efficiently ultimately saves time in the classroom.
Cost
Cost
Most high-quality machine learning tools are not free to use. While there are some that can be used for free, most that are able to deliver high quality machine learning experiences are behind a paywall.
Accessibility
Accessibility
Machine learning has the power to make learning and educaiton more accessible to minority groups, including those with disabilities, language learners, and those with limited resources for supplemental tutors, programs, etc.
Source Material
Source Material
Machine learning on a large scale comes mostly from publicly available data on the internet. This means that the biases, misinformation, and misrepresentations present online also find their way into machine learning tools.
Versatility
Versatility
Machine learning has the capability to recognize images, shapes, text, speech, code, or virtually any input we can think of. This means that it is able to cover a diverse range of needs and uses.
Little Regulation
Little Regulation
Machine learning on this scale is very new, and laws regarding privacy, data protection, copyright protections, and the ethics of machine learning are severly lacking. Protections for vulnerable groups in regards to machine learning are missing, and this requires a degree of caution and scrunity when using these tools.
The Possible Future of Machine Learning
The Possible Future of Machine Learning
Machine learning has the potential to radically change how we approach teaching and education, especially in an online context. As machine learning programs get increasingly more sophisticated, they will be able to influence more and more of our daily lives. While those influences can be positive, it is important to consider the wider impacts of machine learning on our lives and our students.
Machine learning has the potential to radically change how we approach teaching and education, especially in an online context. As machine learning programs get increasingly more sophisticated, they will be able to influence more and more of our daily lives. While those influences can be positive, it is important to consider the wider impacts of machine learning on our lives and our students.
About the Developer
About the Developer
Caroline Aldrich
Caroline Aldrich
Caroline is pursuing her Master's degree in Education with an emphasis in Educational Technology and an Instructional Design certificate. She currently works at an IB school in South Korea where she teaches Design (similar to engineering) to students in preschool-grade 5. She is passionate about new technologies and preparing students for the future using authentic learning experiences in a global context. The thing she loves most about being a Design teacher is how much there is to learn! There is always something new and exciting to do in the field of educational technology.
Research Sources
Research Sources
5 major benefits of machine learning in Education. Inoxoft. (2022, August 9). https://inoxoft.com/blog/how-machine-learning-is-improving-education-benefits/
Anderson, K. (2023, August 22). 5 ways ML and AI is transforming the Education System. ProProfs Training Blog. https://www.proprofstraining.com/blog/machine-learning-ai-is-transforming-the-education-system/
Boettcher, Judith V., et al. “Chapter 3 Best Practices for Teaching Online: 10 Plus 3.” The Online Teaching Survival Guide: Simple and Practical Pedagogical Tips, Jossey-Bass, a Wiley Brand, San Francisco, CA, 2021.
Danish, A. (2023, May 16). Exploring the impact of bias in machine learning: Causes, consequences, and potential solutions. LinkedIn. https://www.linkedin.com/pulse/exploring-impact-bias-machine-learning-causes-potential-ansari-danish
freeCodeCamp.org. (2020, March 31). Want to know how deep learning works? here’s a quick guide for everyone. https://www.freecodecamp.org/news/want-to-know-how-deep-learning-works-heres-a-quick-guide-for-everyone-1aedeca88076/
Khan Academy. (n.d.). Khanmigo Education Ai Guide. https://www.khanacademy.org/khan-labs
LTD, A. (2020, September 24). Appsinvo : How will machine learning save our time. Appsinvo Blog. https://www.appsinvo.com/blog/how-will-machine-learning-save-our-time/
Matthias, D. L. (2021, June 16). Towards adaptive AI with continual learning. Medium. https://medium.com/continual-ai/towards-adaptive-ai-with-continual-learning-f493fd0d698
Orium. (2023, October 26). Making the web more accessible using machine learning. Medium. https://blog.orium.com/making-the-web-more-accessible-using-machine-learning-8a32eaafdb3a
Renukasoni. (2019, July 31). Image detection, recognition and image classification with machine learning. Medium. https://medium.com/ai-techsystems/image-detection-recognition-and-image-classification-with-machine-learning-92226ea5f595
Work, I. at. (2022, January 14). Ten ways AI regulations and standards will evolve in 2022. IEEE Innovation at Work. https://innovationatwork.ieee.org/ten-ways-ai-regulations-and-standards-will-evolve-in-2022/
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