Personalized Learning at Scale: Using Adaptive Tools & Digital Assistants

Concurrent Session 6
Streamed Session

Brief Abstract

Considering today's overstimulated lifestyle, how do we engage busy learners to stay on task? Join this session to discover current efforts in implementing ubiquitous educational opportunities through customized interests and personalized learning aspirations e.g., adaptive math tools, AI support communities, and memory management systems.

Presenters

I am a systems analyst at Arizona State University. I have been at ASU for two years now, and have worked on finding and implementing different adaptive tools and technology to include in our Open Scale Courseware, as well as drive our Earned Admission product to support students as efficiently as possible.
I have been an instructional designer at Arizona State University for three years. I am part of our Design and Development team working on the Earned Admissions project, Master's in Computer Science, as well as on our MicroMaster's project with Thunderbird School of Business. I design courses across three different Learning Management Systems, Open edX, Coursera and Canvas. I am always looking to incorporate new interactive technologies in our courses. I live in Phoenix, Arizona and I enjoy being outside in the sunshine as much as possible.

Additional Authors

Peter van Leusen is the Manager of Instructional Design with focus on scalable and adaptive projects for EdPlus, Strategic Design & Development at Arizona State University. Over the past 16 years, he has worked at multiple large research-focus universities in the US with the goals to foster student success and truly have an impact. His recent instructional design projects include collaborating on adaptive curriculum and courseware development, designing MOOCs in a foreign language, and spearheading innovative digital educational experiences for broad audiences. Before joining ASU, Peter worked as the Assistant Director in the Office of Instructional Consulting in the School of Education at Indiana University and previously taught middle and high school German. Peter holds a PhD in Instructional Systems Technology from Indiana University, Bloomington. His research interests include Faculty Development, Instructional Technology, Instructional Design, and Innovative Teaching & Learning in face-to-face, hybrid, and online formats.

Extended Abstract

Session Learning Objectives (3)

  • Illustrate the implementation of adaptive and active learning in open scale courses.

  • Describe current efforts to develop courses that support personalized instruction at scale.

  • Demonstrate innovative technology that reinforces student engagement and success.

 

Takeaways for Session Attendees

This session will showcase adaptive assessment tools incorporated into Open Scale Courseware (OSC) at Arizona State University (ASU), provide course outcomes (student success) and showcase technical solutions for learning. Takeaways include a proven implementation model, outcomes and experiences representing multiple perspectives, and a scalable solution for achieving student success.

 

Introduction

Everyone is busy! This is particularly true for today's adult learners who often need to juggle many responsibilities, including a job, family obligations, and other life commitments. Fulfilling these obligations, however, comes often at the cost of professional career advancement where additional education credentials are required. Today's adult learners simply cannot disregard their responsibilities to take time off for pursuing the necessary formal education in a traditional campus experience.

One possible solution is the enrollment in online programs offered through accredited universities. In their latest review on the status of online education in the US, Allen and Seaman (2018) stated that enrollment in online courses has been consistently increasing over the past decade with more than 6.3 million learners taking classes in 2016. Although online education offers flexibility for busy adult learners, most universities still set rigorous admission standards often coupled with significant tuition expenses. Meeting these admission standards or costs is particularly a barrier for those learners who previously struggled in their academic experiences or barely earn enough income to make ends meet.

Here, Arizona State University's (ASU) Open Scale Courseware (OSC) offers another alternative to attain an education. Similar to existing Massive Open Online Courses (MOOCs), the OSC platform or website is powered by Open edX and offers users access to first-year online university courses developed by ASU and taught by ASU faculty and instructional teams. In contrast to MOOCs, these courses may be taken either for auditing purposes or in pursuit of transferable university credit. Auditing allows users to explore courses without any option to earn credit or obtain any verified record of participation. In order to gain and maintain eligibility to opt for ASU credit, users must adhere to all associated rules and guidelines governing course participation, academic integrity, academic performance, fees/payments, and credit conversion.

ASU also offers students who were previously denied admission a way to earn conditional entrance into the university. This new opportunity allows learners to successfully complete a series of online courses that count toward their degree. This program is a perfect demonstration of ASU’s charter to include and support all learners and their academic goals.

 

Engaging Busy Adult Learners in OSC

Engaging adult learners is particularly challenging in MOOCs where completion and retention rates are often very low (Jordan, 2015). Reviewing the completion rates of over 200 MOOCs between, Jordan showed that completion rates "vary from 0.7% to 52.1%, with a median value of 12.6%" (p. 341). These low numbers are concerning because effective and efficient online learning generally requires consistent engagement in the learning experience.

To better engage adult learners in OSC, Instructional Designers at ASU sought to integrate established cognitive science principles in their online teaching and learning strategies. In their meta-literature review on applying cognitive science principles to create effective, efficient, and engaging online learning experience, Nilson and Goodson (2017) suggested 25 universal strategies for designing, organizing, and presenting online courses. In particular, ASU's OSC courses were designed where (1) students learn from constant practice and targeted feedback; (2) students learn best in a low-stress and anxiety-free environment; and (3) students learn better when they review materials multiple times in intervals throughout the course. These three principles "focus on how students receive … content… [as well as] drive and sustain the whole process of learning" (p. 79).

 

Applying Cognitive Science Principles

To implement these three strategies, ASU partnered with companies that offered scalable learning tools, such as Cerego, McGraw Hill ALEKS, Gradarius and InScribe. These tools use a combination of artificial intelligence, machine learning, and adaptive technology to provide immediate, personalized feedback and practice to improve memory skills and cognition.

Assessment and LEarning in Knowledge Spaces, (ALEKS) is an adaptive technology conforming to the student’s performance on previously answered algebra questions and presenting problems to students based on their understanding of each question. As the student works through algebra problems, the learning tool is recognizing the student’s patterns and where reinforcement needs to be inserted. ALEKS then instructs the student on the topics she is most ready to learn. As a student works through a course, ALEKS periodically reassesses the student to ensure that topics learned are also retained.

When the student achieves mastery of College Algebra, the Gradarius tool continues to scaffold student learning via interactive calculus problems. Gradarius assists each student at every step of the learning process by offering immediate feedback, guiding problem solving, pointing out mistakes and providing hints. The learning tool mimics the behaviors of a seasoned tutor by providing positive reinforcement and suggestions each step of the way, without solving the problem for the student. Thus, providing the ability to immediately offer every student meaningful feedback and guidance during each step of the problem-solving process regardless of the time of day.

InScribe is a community of learners and educators that work together to provide highly scalable, context specific support. It uses this power of community and artificial intelligence to connect students to the most relevant resources, answers, and individuals they need to succeed in school and beyond. InScribe then captures these valuable interactions, and makes them reusable, so each generation of students benefits from the conversations that came before them. Then, InScribe A.I. analyzes student interactions, proactively escalating high priority issues and students that require direct intervention. Ultimately, experts spend less time in email and discussion forums, and more time with the students that need help most.

ASU currently uses Cerego in nine of our OSC courses, ranging from engineering, marketing as well as social science courses. In each of these subjects, learners demonstrate content mastery either through a web browser, a mobile application, or a digital assistance. All options are is seamlessly integrated either through mobile app or with Open edX through learning tool interoperability (LTI) and in-frame interaction with grade passback. It uses the principles of neuroscience and cognitive science to provide a personalized learning experience, even on-the-go. For example, using the Cerego app, learners can quiz themselves on vocabulary for a Microeconomics course, star patterns in Astronomy, or Excel formulas in a computer applications course. It implements the evidence-supported cognitive science algorithms and intelligent system optimization to deliver the right instructional and quiz content to users at the perfect time to boost retention and engagement. Furthermore, the machine-learning approach constantly adapts, which allows for user-by-user, item-by-item personalization.

In addition, Cerego will utilize Amazon’s digital assistant Alexa to meet learners where they are. This functionality further empowers learners to engage Alexa at any time to quiz them on course materials at home, hence, allowing for ubiquitous learning. This innovation will improve how universities are currently using digital assistants. Higher education as a whole has only used it to answer generalized questions regarding the university. Feedback has shown that students want a more personalized experience, which is exactly what Cerego is proposing to do.  

When comparing students who completed only some memory sets vs. those who have completed all sets, there is a noticeable 10% average difference on the final grade. Student feedback regarding this tool is overwhelmingly positive, e.g., “Cerego is most likely key in retention of concepts and an excellent learning tool” and “[Cerego] took me from having basic knowledge to solidifying my mastery of each section very quickly.” The staff and faculty share the same positive thoughts for the product as the students.

 

Conclusion

ASU is continuously taking strides in finding innovative tools to support student learning. By partnering with these tools, ASU has created a personalized learning experience that can be utilized when the student has a free moment to study. In this session, we will demonstrate how these affordances are implemented in ASU’s OSC courses and, as a result, have shown to engage busy adult learners.

 

Q&A/Group Discussion

  • Asking participants current course structure

    • How they support learning at scale

    • What technologies currently use (if any)

    • How provide personalized experience at scale

    • Do you see using any of these tools in your courses?

 

References

 

Jordan, K. (2015). Massive open online course completion rates revisited: Assessment, length and attrition. The International Review of Research in Open and Distributed Learning, 16(3).

Nilson, L. B., & Goodson, L. A. (2017). Online teaching at its best: Merging instructional design with teaching and learning research. John Wiley & Sons