Adapting Adaptive Learning
Systems in Higher Education
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Table of Contents
Part 1: What are
Adaptive Learning Systems? 5
Definition 5
How it Works 6
Part 2: Why Should We Use
Adaptive Learning Systems ? 8
Benefits 8
Challenges 10
Part 3: Applying Adaptive Learning
Systems in Higher Education 12
Application Examples 12 Conclusion 14
Part 1: What are
Adaptive Learning Systems?
Definition
Adaptive learning is a broad term, and not all educators agree on its definition. Some educators argue that adaptive learning takes place any time a teacher alters their methods to accommodate individual students; whereas, some educators argue that adaptive learning implies the usage of software. For the purpose of this paper, we will focus on the software centered definition of adaptive learning, often referred to as “adaptive learning systems” (als). Essentially, adaptive learning systems are technologies that automatically adapt to the needs of individual students in real time.“
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Digital learning systems are considered adaptive when they can dynamically change to better suit the learning in response to information collected during the course of learning rather than on the basis of preexisting information such as a learner’s gender, age, or achievement test score. Adaptive learning systems use information gained as the learner works with them to vary such features as the way a con-cept is represented, its difficulty, the sequencing of problems or tasks, and the nature of hints and feed-back provided. (U.S. Department of Education, Office of Educational Technology, 2013) Adaptive learning systems are technologies that automatically adapt to the needs of individual students in real time.4
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Adaptive Content
Content based adaptive learning systems are able to analyze the answers of the student and provide further resources based off of those answers such as tailored hints, feedback, and more information on specific elements of a lesson. This type of adaptive learning is tai-lored towards a single task, like multiplying fractions and provides constructive feedback or guiding hints (Open Ideas at Pearson, 2016).How it Works
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daptive learning systems can be broken down into two broad categories, publisher models, wherein all the content is pre-packaged and provided as-is, and platform models, wherein educators have varying degrees of freedom to alter content (Oxman and Wong, 2014). Both types of adaptive Learning Systems most commonly operate by altering one or more of three different categories: adaptive content, adaptive assessment, and adaptive sequencing (Open Ideas at Pearson,2016). Although all three categories share the common goal of improving the learning experience, they all function in unique ways.Adaptive Assessment
Assessment based adaptive learning systems adjust questions based on whether or not a student answers correctly. Most commonly, the difficulty of questions are altered. For example, getting an answer correct would lead to more difficult questioning and getting an answer incorrect would lead to easier questioning. These systems are often used for assessment or bench mark purposes (Open Ideas at Pearson,2016).Adaptive Sequencing
Sequencing based adaptive learning is the most complex of the three categories. They continuously gather data on the student and, often with the use of algorithms or predictive analytics, change the sequence or type of content presented to the student (Open Ideas at Pearson,2016).A
daptive learning systems can function as both stand alone courses and supplemental instruction. Due to the current functionality of adaptive learning systems, most of the adaptive learning systems currently available focus on disciplines with clear cut right and wrong answers. Further research and broader application could accelerate the expansion of adaptive learning systems into more areas of the education system, including higher educatiom. The most effective adaptive learning system for higher education would likely implement a blend of professor and software instruction using a platform and sequencing model.6
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he three main benefits of utilizing adaptive learning systems are: the possibilities for increases in one-to-one instruction, individualization, and data collection.Part 2: Why Should We Use
Adaptive Learning Systems ?
One-to-One Instruction:
Adaptive learning systems can replicate the effectiveness of one-to-one tutoring for students (VanLehn K., 2011). This aspect would be especially beneficial in higher ed-ucation for large undergraduate classes that don’t have many opportunities for 1-1 instruction from professors.Individualization:
Not every student requires the same type of instruction nor do they have the same learning gaps. Adaptive learning systems takes this into account, and provides instruction tailored to, the individual.Benefits
Data Collection:
Adaptive Learning systems automatically collect and analyze student data. This functionality can alert professors of where most students are struggling and which students need the most help (Prusty and Russel, 2011). This information can positively impact the way that educators present information to studentsA
s classroom sizes continue to grow, adaptive learning systems provide a useful and effective way to cater to student needs. “One of the touted benefits of adaptive learning is that students can work on only what they need when they need it” (Open Ideas at Pearson,2016). Adaptive learning systems can provide educators with the frame work to give many students the individual attention they need.8
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lthough adaptive learning systems can benefit learning out- comes, the technology is still a work in progress. There are sever-al roadblocks that discourage educators from applying adaptive learning systems on a broader scale.Publisher vs. platform models
Although platform model adaptive learning systems are becoming more available, a lot of adaptive learning systems are publisher model (Open Ideas at Pearson,2016). Publisher mod-el adaptive learning systems limit the input of the teacher on the lessons provided to students. This limits the extent of which the software can truly adapt to students due to pre-programmed limitations.Challenges
Finite Subject Matters
Most adaptive learning systems currently available only work with finite subjects such as mathematics (Pugliese, 2016). Recently, some adaptive learning systems have expanded into other subjects such as the sciences, engineering, and medical fields. Despite these advancements, adaptive learning systems are currently unable to be applied to most disciplines in the humanities that lack definitive answers or solutions to problems.D
espite these limitations, several companies are working towards overcoming these obstacles and expanding adaptive learning systems beyond finite subjects. If more universities were to implement adaptive learning systems in their curriculum, more students could benefit from adaptive learning systems that are already available and more research towards expanding adaptive learning systems’ capabilities could be explored.10
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Part 3: Applying Adaptive Learning
Systems in Higher Education
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espite the benefits, higher education has been slow to adapt adaptive learning systems due to some of the difficulties of such a task (Pugliese, 2016). There are, however, some excellent examples of adaptive learning systems being effectively implemented in higher education. Smart sparrow is one of the companies at the forefront of adaptive learning systems. It started out as an offshoot of a research group (The Adaptive Learning Group) in 2011 and is now one of the most innovative adaptive learning system platforms. Smart Sparrow goes beyond simple algorithms to support students and actively involves educators in the creation and management of the content provided to students. Smart Sparrow provides the platform and instructions for educators to create adaptive learning system experiences that are based om the unique data of their classroom. Although the limitations of adaptive learning systems still apply in some compacity, the freedom that Smart Sparrow provides to educators allows it to be applied to a broader range of subjects than many other available programs cannot. Smart sparrow has partnered with both the corporate and educational worlds to create unique and interactive adaptive learning systems.Application Examples
Prusty andRussel’s research confirmed that Smart Sparrow’s teacher driven sequencing and platform model adaptive learning system provides significant results. For this research, professor Prusty of UNSW, a leading Australian university, partnered with Smart Sparrow to create threshold concept tutorials for a first and second year mechanics course. These tutorials led to a reduced failure rate, from 31% to 7%, over time and an increase in student’s achievement of a High Distinction, from 5% to 18%. These tutorials have been improved with more data over the years and are still available to assist students from all over (2011). Applying teacher and student-centered adaptive learning systems in more universities could lead to an overall increase in learning outcomes for students and further advancements for adaptive learning systems.
Figure 1 Improved Student Achievement as More Adaptive Coursework is Introduced Over Time (Prusty and Russel, 2011)
