Learning Pathways in Education

The Rapid Rise of Learning Pathways

We have access to virtually unlimited information at our fingertips these days. Sound instructional design takes all of this information that is whizzing by in all directions and creates structure around it. This structure focuses on concepts consistent with how people learn. Traditionally, this occurred through macro learning opportunities like classes, degrees, and classroom training programs.

Advancements in technology have allowed two disruptive innovations to emerge: Microlearning and Personalized Learning. These developments are of interest to learning leaders and L&D professionals who aim to equip their employees with the most relevant information while reducing the time, and ultimately money, that is spent on workforce development. At the same time, employees are looking for ways to engage in asynchronous instruction that is tailored to their current knowledge and builds towards complete mastery.

Pause and reflect on that last sentence for a moment. The ability to have a scaled personalized experience is truly revolutionary. This personalized learning was typically only realized through instructional models such as cognitive apprenticeships (Collins, Brown, & Newman, 1988) or scaffolded instruction (Pea, 2004). Software developments are now at a place where adaptive learning goes beyond branching logic. Learners can now engage with small chunks of content in a way that is customized to them. When we think about the structure of this type of learning environment, it becomes clear that there is a need for some direction and planning in order to sequence a learning experience. A key aspect of the eLearning designer’s role then, is to build this structure and avoid the "one-size-fits-all" approach.

People like to see a path toward progression. This is true regardless of the circumstance. They could be progressing in their career but it is just as likely that they want to progress in a hobby or talent. In our current educational world, learning pathways help to accomplish this progression. According to Rughinis (2013), learning pathways provide both tails and conditionality for learners. In other words, they help learners to see where they have come from and where their learning path is expected to go.

In the workforce, an ideal learning pathway focuses on the current needs of the learner, where they would like to go in their career, and what skills the company needs them to acquire now. Creating sound learning pathways requires that the eLearning designer give over some level of control to the learner. They must set goals and choose from a variety of options in order to accomplish their goals.

An eLearning designer in a learning pathway system must think of themselves as curators and connectors of knowledge.

What Might a Learning Pathway Look Like? Characteristics

We can think of Learning Paths much like a road trip. The best road trips typically start with directions, but the driver must be able to adapt to changes in road conditions relative to the map. In order to do this, they rely on mile markers, exit signs, and even a compass in order to arrive at their destination.

Are there other ways to take a road trip? Sure, you might aimlessly drive around to see different things or you might be so reliant on your structured GPS that any changes in construction cause you to get lost.

A good learning pathway looks an awful lot like the first road trip. The eLearning designer will identify and make explicit the learning objectives and sequence these together in a logical order. However, the learner has the ability to control which direction they take within their journey towards professional development.

Learning pathways must have strategic opportunities to build in prior knowledge, reflection, and application prompts. Without these, the learner is likely completing micro modules to learn the content but would be unable to connect the content to the bigger reason of why they are completing those modules. A learning pathway creates an environment to integrate these design features.

Learning pathways are intended to be flexible, multidisciplinary, and increasingly personalized. When there is a need for more than 1 or 2 micro modules, learning pathways are necessary. They pull together all of the relevant information into a longer learning experience.

·      These roadmaps are flexible because each employee engaging in professional development can choose their own path.

·      They are multidisciplinary because our jobs are multidisciplinary and people need to be able to show competency in more than one area.

·      Finally, they are increasingly complex and personlized in order to bring the learner along from beginner to expert.

It's all About Learner Control

Well-designed learning pathways do a few things for learners:

1.   They give the learner a place in which to track progress made toward learning goals.

2.   They move the learner toward identified learning objectives.

3.   They provide a sense of empowerment for the learner. Rather than being given a standard course, learners can choose their learning goals, have flexibility to adapt goals if necessary, and earn recognition along the way.

How to Start Creating Learning Pathways

As an eLearning designer in charge of creating learning pathways, there are a few best practice suggestions to take into consideration:

1) Make connections between stops

The learner is going to be immersed in learning at a granular level. The connections between different activities are not apparent. The eLearning designer needs to look at all of the stops on the learning journey and make connections between them for the learner.

2) Create Scalable Modules

In order to create scalable modules, the eLearning designer must start in a system that is inherently scalable. Choose wisely and focus on systems that allow for a flexible curriculum that can be assembled in different ways.

3) Provide Strategic Prompts

A well-designed learning pathway does two things. It requires learners to activate prior knowledge and to reflect on their initial experience after they have completed a certain module. The eLearning designer needs to build in prompts that focus on the learner and force them to think in a way that applies what has just been learned to future situations.

4) Create a Visual Representation of Learning

Just like mile markers represent a way of marking the way to the destination, a learning pathway needs posts to keep learners focused and on target. This helps them see the progress they have made and what still needs to be done in order to complete a pathway.

The Future of Learning Pathways

There are several technology trends that are going to impact future learning pathways. Artificial intelligence will definitely have implications for the types of questions that can be asked and how answers are evaluated. Question types are currently limited in asynchronous, self-guided instruction. This will change when artificial intelligence is included in learning pathways. Personalized learning is impacting how people expect their educational experience and how CLOs view quality eLearning design. This type of learning is shown to save time and ultimately money by delivering content at the learner’s current level. It allows for ‘mass customization’ of learning.

PLAiTO AI designs the learning path considering the learning gaps from other concepts which might be useful for the current concept. This individualized cross-concept learning path helps the student understand the concept in a deep and efficient manner.

ADAPTATION OF ARTIFICIAL INTELLIGENCE IN EDUCATION SYSTEM

When we think of artificial intelligence, there is a hardwired imagery of gigantic thinking machines working in sci-fi environment. This imagery often comes from the science fiction that we have been watching or reading since childhood. However, deep diving suggests that artificial intelligence is an advanced form of algorithm that empowers machines to emulate human behaviour under real life situations. Today, there is no industry untouched by the ripples caused by artificial intelligence.

Education sets the foundation of human behaviour. Educational ecosystem is formed by knowledge base, teaching skills and experience, learning capability, and evolving teaching methods. Digital learning solutions equipped with artificial intelligence are making revolutionary changes to the way education is imparted in students with varied interests and capabilities.

Here are some facets of education where we can feel the difference created by artificial intelligence:

Personalized learning

Every child is special. Therefore, getting all of them through the blackbox called education is not fair. Even if a teacher tries her best, time available in the classroom and school may not be enough to attend to the learning needs of every student. With artificial intelligence, we can apply high degree of personalized learning wherein each student learns at her pace and remediated specifically for their weakness and not a class average. Such a learning solution can evaluate the understanding of a student about each parts of curriculum and emphasize on those that are not already mastered. At the same time, it also supplements the effort of a human teacher.

Elevate the role of teachers as facilitators/motivators

Apart from the core teaching role, teachers often handle several mundane administrative responsibilities such as student attendance management. This reduces the efficiency and effectiveness of teaching. However, artificial intelligence allows automation of these tasks so that teachers can play their real role- to be the motivator and guiding force for learners. This could lead to a significant rise in quality of education and reshape the future.

Transparent and objective assessment

With due respect, rigid conventional assessment methods have ruined more young lives than nurtured. Simply put, if every student has different pace, acceptance, and aptitude towards the curriculum, it is unfair to assess them with the same yardstick. However, teachers often do so because of their inability to apply personalized assessment. Artificial assessment comes handy here. An assessment solution that runs a robust database and analytics engine at the core can analyze millions of complex patterns in the individual learning and assess the learner accordingly.

Creation of smart content

Digital content is rapidly finding acceptance in traditional classrooms. However, this also means that the content needs to be created, customize, and updated in real time. Given the variety of subjects and learning streams, this is a herculean task for humans. Artificial intelligence could be of great help here. With advanced artificial intelligence capabilities, we can digitize textbooks or create learning digital interfaces that are relevant to students of all age and grades.

A personalized adaptive learning platform that fulfills every required attribute of intelligent learning is PLAiTO - LearningPersonalized.

Visit us at: https://www.plaito.ai

Artificial Intelligence and its Use Cases in Publishing

Everybody is talking about Artificial Intelligence (AI) right from Google, Facebook, Amazon

to small companies. There are many tech startups trying to solve industry challenges through AI solutions. Most of these companies are acquired by the big companies to scale up their AI capabilities and use the solutions to solve their own challenges or innovate new products.

What is AI? In the simplest terms, AI is the part of computing that gathers information from us, from the online world, and importantly learns from the information collected. Most of the AI solutions available in the market, study the data collected from our daily information consumption and give us recommendations to suit our convenience. Sounds simple but behind the scenes, there is a complicated algorithm that runs and displays the desired results. This may sound familiar if you are using apps like Siri, Ok Google and Netflix.

AI technology has been in existence for quite some time. Amazon and other big retailers 

have been using AI to learn individual purchase history and recommend other products,

similarly, Netflix suggests videos and shows based on the individual’s past viewing history.

So, we have been consuming AI technologies, we need to just figure out how these use

cases can be used for the publishing industry.

AI and its technologies are hot buzzwords today, everybody is talking about it without understanding the true meaning and its power. Artificial Intelligence, Machine Learning, Natural Language Processing are often interchangeably used. There are fundamental differences between each and it is important to understand them before you plan to work on them.

For all the publishers with limited technology background, let the techies not take you for a ride, here are a few basics for you to keep you engaged in the technical conversation.

Artificial Intelligence (AI) helps in building systems that can do intelligent things.

Subset of AI are:

• Machine Learning (ML) helps in building systems that can learn from experience.
• Natural Language Processing (NLP) helps in building systems that can understand language.
• When NLP and ML are used together, it helps in building systems that can learn how to understand language.

Search
Most of the publishers have gone online with user focused digital platform. Many open search technologies like Solr, ElasticSearch, LucidWorks, OpenSearch and so on are already being used by the publishers. AI or to be more specific machine learning algorithms if used for search can help your end users get the right information within few seconds. The key would be to build machine learning algorithms that learn from user behavior and provide information in various formats including text, pdfs, images, videos and other digital assets.This is a sure shot winner to increase customer experience and to earn your brand loyalty.

Smart recommendations

Considering the same use case from Netflix, AI can be used by publishers to recommend

articles, research papers and other relevant resources to the user based on their search or

past usage history. Combine this with semantics and the users can get exactly what they

have been looking for.

Personalization

Machine Learning algorithms can learn the behavioural patterns of the users and

personalize the content to deliver the right message to the right audiences. For instance,

some users may like the information in the form of graphs, numbers or visuals rather than

long text, the algorithms can learn the user pattern and personalize their content

consumption showcasing the desired format at the top.

Short reviews and summaries

The attention span for an individual has further reduced to 8 seconds. This is all we have to

grab the user’s attention and make him stay longer on the website. Especially in the case of

journals, chapters, research papers, academics or stories, a short review or summaries

could help the user decide and stay longer. Using Natural Language Processing (NLP),

coherent and accurate snippets of text could be produced from longer pieces.

Customer Service

The AI chatbots, voice search or AI assisted human agents are improving quality of the

customer service. Be it a researcher, editor, librarian or a student, the enquiries would differ

based on the user and other demographics. With the help of deep learning ML and NLP,

algorithms can be developed to provide the right answers in no time. Combine this with text

analysis and computational linguistics, you can take a step forward with sentiment analysis

to decipher your user’s mood and react accordingly.

Use of Social Media

Leverage the power of machine learning to optimize social media channels, identify the right

target audience, personalise the content and timing for your post. Use NLP to understand

and analyse the social behaviour and with the combination of both the technologies,

manage your reputation before it gets out of hand.

Automate internal processes

Publishing workflow includes all teams – Publishers, Editors, Production, Legal, Developers,

and Marketing. Each team has their own internal workflows and processes. Replacing some

of the manual processes with automation can reduce time to market. For example, an

algorithm to peer review the research papers, or conduct a copyright test or do a reference

check can reduce the workload of the teams and time taken to do the tedious manual job.

Security

Let’s not forget security. Ransomware, Sci-hub and other hacking attempts are a result of

some vulnerabilities that have been ignored on our digital platform. Machine Learning

algorithms can focus on prediction and easily detect a known attack learnt from earlier data.

There may be many more AI uses cases publishers may be working on, but this is an easy

start. However, just understanding AI its use cases are not going to move the boat forward.

You need to build a proper infrastructure and a conducive environment to build AI solutions.

You need the right skills, software technologies, hardware technologies with a strong vision

to build AI solutions. It needs time, patience, efforts and commitment to be a forerunner in

technology and ahead of the competition.

Putting these jigsaw pieces together, many companies are embracing AI technologies to

accelerate their digital journey. AI becomes meaningful and impactful when it has access to

large amounts of high-quality data and is integrated into automated work processes. AI is

not a shortcut to these digital foundations but is a strong powerful extension of them.

A platform that is inscribed with all the above mentioned attributes is PLAiTO – Learning

Personalized.

Visit us at: https://www.plaito.ai          

Personalized Adaptive Learning platforms future of edtech sectors.

Education technology plays an essential role in schools today. Whether the technology supports instructional intervention, personalized learning, or school administration, the successful application of that technology can dramatically improve productivity and student learning.

That said, too many school leaders lack the support they need to ensure that educational technology investment and related activities, strategies, or interventions are evidence-based and effective. This gap between opportunity and capacity is undermining the ability of school leaders to move the needle on educational equity and to execute on the goals of today's K-16 policies.

The education community needs to clearly understand this gap and take some immediate steps to close it.

So what needs to be done?

Here are five specific issues that the education community (philanthropies, universities, vendors, and agencies) should rally around.

Set common standards for procurement.

If every leader must reinvent the wheel when it comes to identifying key elements of the technology evaluation rubric, we will ensure we make little progress - and do so slowly. The sector should collectively secure consensus on the baseline procurement standards for evidence-based and research practices and provide them to leaders through free or open-source evaluative rubrics or "look fors" they can easily access and employ.

Make evidence-based practice a core skill for school leadership.

Every few years, leaders in the field try to pin down exactly what core competencies every school leader should possess (or endeavor to develop). If we are to achieve a field in which leaders know what evidence-based decision-making looks like, we must incorporate it into professional standards and include it among our evaluative criteria.

Find and elevate exemplars.

As Charles Duhigg points out in his recent best seller Smarter Faster Better, productive and effective people do their work with clear and frequently rehearsed mental models of how something should work. Without them, decision-making can become unmoored, wasteful, and sometimes even dangerous. Our school leaders need to know what successful evidence-based practices look like. We cannot anticipate that leader or educator training will incorporate good decision-making strategies around education technologies in the immediate future, so we should find alternative ways of showcasing these models.

Define "best practice" in technology evaluation and adoption.

Rather than force every school leader to develop and struggle to find funds to support their own processes, we can develop models that can alleviate the need for schools to develop and invest in their own research and evidence departments. Not all school districts enjoy resources to investigate their own tools, but different contexts demand differing considerations. Best practices help leaders navigate variation within the confines of their resources. The PLAiTO - Learning Personalized is one example of a set of free, open-source tools available to help schools embed best practices in their decision-making.

Promote continuous evaluation and improvement.

Decisions, even the best ones, have a shelf life. They may seem appropriate until evidence proves otherwise. But without a process to gather information and assess decision-making efficacy, it's difficult to learn from any decisions (good or bad). Together, we should promote school practices that embrace continuous research and improvement practices within and across financial and program divisions to increase the likelihood of finding and keeping the best technologies.

The urgency to learn about and apply evidence to buying, using, and measuring success with ed tech is pressing, but the resources and protocols they need to make it happen are scarce. These are conditions that position our school leaders for failure - unless the education community and its stakeholders get together to take some immediate actions.

While this is a critical time for evidence-based and effective program practices, here is the rub: The education sector is just beginning to build out this body of knowledge, so school leaders are often forging ahead without the kind of guidance and research they need to succeed.