Personalised Learning: What's Coming And 10 Trends Every Educator Needs To Know About

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February 8th, 2015 No Comments Features

personalised learning

Personalised Learning is the tailoring of pedagogy, curriculum, and learning environment to meet the needs and aspirations of individual learners. In a personalised learning environment, learning starts with the learner, not the instructor. The learner understands how she learns best, so she is active in designing her learning goals. The learner has a voice in how she will access and acquire information, and a choice in how she will express what she knows. When she owns and takes responsibility of her education, she is more motivated and engaged in the learning process.*

Many educators believe personalised learning has the potential to drive breakthrough results for students, and as a result movements have begun cropping up all over the world.

A Growing Movement

Personalised Learning is currently being road-tested in schools all across Australia. The not-for-profit organisation Big Picture Education is running 40 programs across the nation, including three whole-of-school operations in Tasmania, one in Victoria, and one in West Australia. Many of them have had marked success already.

In December, New York City-based EdTech veteran Knewton raised $51 million to continue its mission of democratising top-tier, personalised education around the world. Knewton’s unique analytics engine is able to map each student’s strengths and weaknesses over time, which then both enables teachers to identify and predict knowledge gaps and personalise instruction and tailored content to each student’s individual learning path.

Also in December, IBM and Gwinnett County Public Schools in Georgia announced a first-of-a-kind research and development relationship that leverages big data, deep analytics, and cognitive technologies to generate actionable insights for personalised education and learning pathways for students. Known as Personalised Education Through Analytics on Learning Systems (PETALS), the project will use machine learning, predictive modeling, deep content analytics, and advanced case management to identify learning needs of students and recommend personalised learning pathways.

The goal of the PETALS project is to move from a ‘one-size-fits all’ model of education toward a truly personalised approach that motivates and engages learners, and ultimately lead to significant improvement in key educational outcomes, such as reducing student drop-out rates; improving academic performance/college readiness; increasing student engagement; and enhancing teacher effectiveness.

The cognitive system being developed by IBM scientists aims to use population analysis of longitudinal student records extracted from a variety of sources. The goal is to identify similar students based on their pattern of learning, predict performance and learning needs, and align specific content and successful teaching techniques. The system uses deep content analytics to automatically label digital learning content with instructions from curriculum standards, thereby significantly easing the challenge of retrieving the right content suited to a specific student’s needs.

 The goal is to identify similar students based on their pattern of learning, predict performance and learning needs, and align specific content and successful teaching techniques.

It also learns from successful intervention practices to recommend personalised pathways for students based on a unique understanding of each student’s context. Anonymised data from close to 200,000 students over 10 years are already available for analysis. The project seeks to harness the power of big data and analytics to provide timely, personalised, and actionable insights to teachers and students, helping enhance their pedagogical and learning experience.

Saddleback College in Southern California, which enrolls nearly 40,000 students, has developed a software called SHERPA, or Service-Oriented Higher Education Recommendation Personalisation Assistant. SHERPA works similarly to the recommendation services on Netflix and Amazon. Student preferences, schedules, and courses can be stored to create profiles that are responsive to student needs.

Building on SHERPA’s course selection tools, Purdue University developed an early warning system for college course-taking success, named Signals. The Signals software monitors students’ behavior patterns and academic performance to determine if they are at risk of earning a low grade and allows faculty to intervene with suggestions on actions they can take to help students improve their grades. An intuitive stoplight dashboard provides indications to students, on their course homepage, if they are underperforming, and prompts the students to take action.

Signals scrapes and analyses data from grade books and activity log files, adding in student demographic information to create a profile of the student that can be compared with those of successful students. At-risk behaviors and characteristics can be identified and guidance and resources provided to invigorate student effort and provide better academic prep. The result is that students are able to have a very fine-grained sense of how they are doing in the course overall and adjust to produce better results or reach out to available resources such as faculty or tutors for help.

Personalised Learning has reached the teacher education field as well. A 2012 report by Australian Catholic University found that meaningful student learning experiences can be achieved through a personalised approach which also supports the emerging tenets of effective, pedagogical use of ICT for learning.

A 2012 report by Australian Catholic University found that meaningful student learning experiences can be achieved through a personalised approach which also supports the emerging tenets of effective, pedagogical use of ICT for learning.

Personalised Learning is only gaining momentum, and it will be an exciting year to get involved if you haven’t already. On that note, what new trends are on the plate for 2015?

Predictions

1. Mainstream Adoption: As more education institutions demonstrate what is possible, momentum will grow for fundamental redesigns of instructional models that incorporate the attributes of personalised learning. Watch for more widespread adoption as experimental models yield positive results.

2. Collaboration Websites: Tabtor, a leader in iPad and tablet-based personalised learning platforms, has created a learning platform that allows teachers to provide a highly personalised learning experience for their students. In addition to explaining the benefits of providing personalised learning, the website–HowLearningWorks.org–will act as a conduit to encourage dialogue, discussion, and sharing of best practices among teachers, parents, and students across the globe.

3. Technology-Granted Time: When teachers can spend more time with individual students and small groups, and have access to better information and instructional supports, their students learn more. Teachers are embracing technologies that help them free up time to focus on what matters most for each of their students, and this will accelerate in 2015.

“It’s likely that some voices will continue to sound alarms about schemes to replace educators with computers and algorithms,” says Stacey Childress, CEO of NewSchools, “but the growing number of teachers who incorporate digital content and tools into their instructional practice will provide a strong, fact-based counterpoint to such claims.”

4. Big Data: We know what Big Data and Learning Analytics can do, but in 2015 we’ll actually start seeing them doing it. Teachers will be able to diagnose learning and thinking styles, adjusting their lesson plans accordingly, and students will receive immediate feedback on a deeper, more efficient level than we’ve seen before.

5. Learner Agency: According to Kathleen McClaskey and Barbara Bray, co-founders of Personalize Learning, the future of personalised learning holds more opportunities for students to take centre stage: “Teachers will devise multiple ways to give learners their voice in lesson design, to ask them how and what they want to learn, to interview them about their learning, to encourage them to participate in class discussions, to choose the appropriate tools for anytime and anywhere learning, and to invite them to reflect on their learning,” McClaskey and Bray predict.

7. The Maker Movement: Encouraging learners to take ownership over their education means giving them a choice. Innovation helps students make sense of their learning. It allows them to be more than passive consumers of knowledge – and instead become creators. McCloskey and Bray urge educators to keep an eye on the Maker Movement, especially when it comes to the influence of 3D printers and how they can provide more opportunities for personalised learning.

8. Mandated Flexibility: “I always tell people to mandate flexibility, which is an oxymoron,” says educator and TED speaker Fred Bramante. If you take out time requirements and put in competency-based regulations that make learning flexible, he explains, then students will take advantage of the independence and creative freedom you have provided for them.

9. Wall-to-Wall Academies: The Clay County School District in Florida is in the process of implementing “wall-to-wall academies” in which personalised learning plans are developed for each student as early as junior high school. Focusing on careers by the time students are high school freshmen is particularly critical, because failure, absenteeism, and discipline referrals are highest in ninth grade than in any other year. Additionally, stakeholder buy-in, enhanced staff professional development, and business and community partnerships will be among the keys to implementing a new strategic plan focusing on personalised learning.

10. Personal Learning Plans: A new law in Vermont calls for every student in grades 7-12 to create, with adult help, a personal learning plan based on that student’s interests and ambitions. The so-called PLP must include not just a list of courses leading to a job or career, but also experiences outside a school setting.

We are riding on the crest of a great revolution in education. With a little guidance–creating guidelines for how data should be treated; reviewing data to find areas where the information might help students make better choices; funding the development of personalisation tools through competitive grants–not only will educators start to see student motivation on the rise, but the students themselves will start to value learning as a personal choice. Let’s catch this wave together.

If you’d like to keep up with the discussion on personalised learning, visit www.personalizelearning.com.

*This paragraph has been adapted from Chapter 1 of the book Make Learning Personal: The What, Who, Wow, Where and Why, which can be purchased here.

About 

Saga Briggs is an author at InformED. You can follow her on Twitter@sagamilena or read more of her writing here.

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