There is a lot of energy and money going into solving the problem of delivering a more personalized learning experience for students. While learning and cognitive research validate this direction in education, many are still struggling to find a way to use technology in service of teachers rather than as a replacement for teachers. A part of the issue is the vast number of scenarios involved in a “personalized” experience for students and teachers in so many educational contexts. For those trying to tackle this issue, instead of reshaping and redefining the problem for the extensive use cases, why not look at what is currently being done at scale and is working?
In 2016, Carnegie Learning, the University of Memphis, and Tutor.com, supported by the Advanced Distributed Learning initiative of the U.S. Department of Defense, joined together to better understand the relationships among college students, Intelligent Tutoring Systems (ITS), and human tutoring. So far, the study led by Carnegie Learning involves two scaled systems that have already been shown to deliver results in student persistence. Results show that students who take advantage of human tutoring are struggling and often frustrated, but more likely to complete a course and persist (Ritter, Fancsali, Berman, and Yudelson, 2016). The story told by this study of struggling, frustrated students is familiar to both K-12 and higher education institutions, but for students not taking advantage of the adaptive and human tutoring learning environment the story often ends with them dropping out of high school or not further pursing their education. So what is in this learning environment that is leading to a happier ending of persistence?
Carnegie Learning’s MikaTM creates the personalized learning path for the student through the use of assessments and adaptive learning technologies. Technologies like MikaTM can track areas of weakness, provide opportunities for practice and immediate feedback, and it can be adaptive – raising or lowering the difficulty level based upon student choice of answers. But the human element is essential to understand student frustration or motivational challenges, or the concepts that the student is missing as he or she works their way through the exercises. Tutors can help students navigate the work path, cheerlead along the way, and coach through the tough parts of the learning journey. Tutor.com tutors deliver that human touch by adhering to learning research that is situated in an online tutoring context. Essentially they use good pedagogy for each individual student.
Tutors are able to simultaneously focus on ways to address student persistence and motivation while meeting the student where they are in their learning to determine a pathway to success. Tutors can deliver this student experience because they have the pedagogical content knowledge of their subject to be adaptive to individual student needs. Together, MikaTM and the tutors provide students with a safe place to fail and thrive, which is the cycle of learning.
So what can be learned from this study and the relationship among students, ITS, and human tutors and possibly generalized to the plethora of scenarios that exist? Educators alone cannot personalize for every individual student in every course which is why services like Tutor.com are such a valuable supplement to the work of the teacher and to the work of adaptive learning software. Tutor.com tutors work at scale—serving thousands of students each week, in over 50 subjects. Each student brings to the online classroom his or her own frustration or desire to expand knowledge, particular challenges to understanding concepts, individual learning style, mindset and level of confidence. Tutors used to working with this span of issues, and available when needed, are able to use their extensive experience with this breadth of pedagogical challenges to effectively support and enhance the work of both teachers and self-directed software. Given this breadth of experiences, perhaps we should try to better understand the pedagogy essential for teachers to interact with adaptive learning technologies and how to build the pedagogical content knowledge of teachers in physical classrooms to leverage the technology. If the technologies are to work in service of teachers, then we should focus on the knowledge and tools teachers need to best leverage this technology.
Ritter et al. (2016). How Mastery Learning Works at Scale. Retrieved from http://learningatscale.acm.org/las2016/wp-content/uploads/2016/05/Ritter_HowMasteryLearningWorksatScale.pdf