What is Machine Learning?

Machine learning refers to computers that are able to act and react without being explicitly programmed to do so. Computer scientists and engineers are developing systems that not only intake, retrieve, and interpret data, but also learn from it. To do this, the machine must make a generalization, using algorithms to perform accurately on new examples after being trained on a different learning data set — much like a human learns from experiences and uses that knowledge to respond appropriately in a different encounter. In this sense, machine learning is widely considered by many researchers and thought leaders to reflect an emerging approach towards human-like artificial intelligence. Practical speech recognition, semantic applications, and even self-driving cars all leverage machine learning. A recent incarnation of machine learning is software called Xapagy, which improvises dialogue and plot moves in stories fed to it by users. The potential of machine learning for education is vast, facilitating altogether smarter technology that has the accuracy of a computer and the adaptability of the most intelligent human beings.

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(1) How might this technology be relevant to the educational sector you know best?

  • add your response here
  • This session description from the IEEE International Conference on Machine Learning applications in Education lists some examples of relevance to education. http://www.icmla-conference.org/icmla13/S03v03.pdf and Use of machine learning techniques for educational proposes: a decision support system for forecasting students' grades:
    http://link.springer.com/article/10.1007%2Fs10462-011-9234-x#page-1 Also, I am not sure if this is the proper place for this comment, however I am wondering if there is any advantage in looking at how some of the technologies listed could actually work together/intersect with one another. As an example looking at how Learning Analytics, Machine Learning, Big Data and Internet of Things might support each other?- jmorrison jmorrison Jan 24, 2014
  • Machine learning starts with the seemingly simple interface of a Google search window, but as we know drills into response alogorithms that provide results or provide recommendations. In all cases of machine learning the interface to learning is an alogorithm. So while there may be huge potential for education, one of the biggest needs is for students to understand that until AI is full emerged, the human input to the process is key. Students need to be engaged in computational thinking,

(2) What themes are missing from the above description that you think are important?

  • I don't know that this theme is missing, but I'd like to amplify the above since I work in this space. Machine learning is really coming to the fore in the context of the instructional improvement systems adopted by RttT states and local districts. The goal of these systems is to close the loop between teacher, curriculum goal, instructional resource, and student outcome. Closing this loop depends on three main factors: 1) profiles, both teacher and student; 2) vetted instructional resources upon which learning experiences may be differentiated; and 3) fidelity of implementation. The system captures usage and implementation data on the resources, integrates it with student and teacher characteristics, and generates data from which conclusions can be drawn. Machine learning can do a lot of give some insight into 1 & 2, but fidelity (3) is something that it tough to capture, even with direct observation. Even so, developing predictive algorithms for learning resource quality is very important to scale effective digital learning. - marcia.mardis marcia.mardis Feb 4, 2014
  • Positioning machine learning in the debate about AI and the proposed emergence of the singularity is important. - judy.oconnell judy.oconnell Feb 8, 2014

(3) What do you see as the potential impact of this technology on teaching, learning, or creative inquiry?

  • The goal of using machine learning in education is to increase the time teachers spend on applied activities and decrease the time spent on direct instruction. It remains to be seen if this outcome will actually be the case. - marcia.mardis marcia.mardis Feb 4, 2014
  • Supporting flexibility immediately and responsively, and allowing inquiry to progress in multiple pathways via creative enquiry. - judy.oconnell judy.oconnell Feb 8, 2014

(4) Do you have or know of a project working in this area?

  • Curriculum Customization Service (CCS) in Denver; CPALMS in Florida; Brokers of Expertise in Californina--all projects contain proile and resource paradata to some extent and are analyzing it using machine learning approaches. See esp. papers by Wetzler, Sumner et al. - marcia.mardis marcia.mardis Feb 4, 2014
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