From Learning Machines to Adaptive Learning Technology


September 10, 2015 by idinnovation

2152048926_d60b8ea093One might wonder how it is that we got here. What emerged prior to adaptive learning technology? The short answer – a teaching machine. A teaching machine is an electronic system that follows a straight path of instruction that aims to strengthen positive behaviors of students (Kara & Sevim, 2013).

In the Horizon Report 2013 Higher Education Edition it was determined that refining of learning analytics would occur in the mid-term range of two to three years – in 2015 it is still emerging (Horizon Report, 2013). Adaptive learning technologies are defined as a complex, data-driven approach to “instruction and remediation” which allows the technology to adjust to learner knowledge and performance and subsequently anticipate what content and resources are useful in transferring knowledge to the learner to facilitate the understanding of the material that is being taught (New Media Consortium, 2015).

Institutions of higher education have mostly been limited to research, development and pilot programs, which positions this innovation as a long-term goal for organizations. Adaptive technologies can be used in academia and various other industries seeking to monitor and develop learners. Educational leaders have been vocal in the need for adaptive learning technologies to easily integrate into current learning management systems.

Widespread use of adaptive learning technologies is at least four years away. Numerous studies, however, indicate that adaptive learning technologies have the potential to transform the traditional learning paradigm and encourage the development of new standards and best practices (New Media Consortium, 2015). It is essential for institutional leadership to allocate funding for the development of appropriate infrastructure, key personnel and experts who are knowledgeable and capable of designing educational curriculum and managing adaptive learning systems.

With adaptive learning, students can benefit from personalized curriculum repositories, targeted content, and institutions can effectively utilize personalized analytics to transform its pedagogical practices with powerful analytics. Simulation games can positively impact students desire to learn, provide immediate feedback, and assist in the development of skills and facilitate the transfer of knowledge (Gaggioli, 2011 p. 625).

Nguyen and Do (2008) contend that adaptive learning technologies provide data about the user and create a model based on the user information. Various questions must be answered to determine the best data gathering method such as: what information shouldinfographic-adaptive1 be obtained and how?, should the system trust the learning to determine appropriate method of instruction and what needs to be adapted (Kara & Sevim, 2013). Designers of adaptive learning technologies must answer these questions and test and integrate appropriate methodologies. A method for avoiding pitfalls of adaptive learning technologies is learning what works for user adaptation by conducting an analysis of big data that examines learner experiences, progress and educational successes for an extended period. Understanding how big data affects individual learners and has the potential to shape pedagogical practices.

Let’s take a closer look at some of the platforms and publishers. Knewton provides a platform that enables consumers to build proficiency-based learning resources Knewton, 2015). Smart Sparrow developed from ongoing research and development and a dissertation on adaptive eLearning by Dr. Droe Ben- Naim at the University of South Wales. His research focused on intelligent tutoring, educational data mining with a focus on instructional technology systems, cognitive learning theory, instructional system design and strategies and instructor pedagogical strategies (Smart Sparrow, 2015). Cerego uses credible learning science research to develop adaptive learning technology focusing significant time in language learning and K through 12 students learning.


  • Gaggioli, A. (2011). CyberSightings. Cyberpsychology, behavior, and social networking, 14(10), 625–626. Retrieved from the Walden Library databases.
  • New Media Consortium. (2015). NMC Horizon. Retrieved September 10, 2015, from
  • New Media Consortium. (2013). NMC Horizon. Retrieved September 10, 2015, from
  • Kara, N. & Sevim, N. (2013) Adaptive learning systems: Beyond teaching machines. Contemporary Educational Technology,4(2), 108-120.
  • Nguyen, L. & Do, P. (2008) Learner model in adaptive learning. Proceedings of World Academy of Science, Engineering and Technology, 35, 396-401.



One thought on “From Learning Machines to Adaptive Learning Technology

  1. Lynn Millard says:

    Thank you! I had been meaning to look that up and I know you are thorough in your research. I was hoping the adaptive learning was coming sooner, but based on what I see it is going to take a change of mindset as well with instructors. The teachers I work with are doing credit recovery for students who have had a bad year or two due to a wide variety of reasons. It will have to be proven to be efficient and students are not finding ways to get out of work easy or early. That is what I see when I listen to teachers working with the make up work that is technology driven. I would go so far as to say that in some cases, making the students go through the steps is part of making sure they spend the time to learn it. Teachers are going to need to see research, data driven results that it works.

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