QuantHub is an AI-powered learning and development platform that focuses on providing data fluency in corporate and educational settings. Rather than create a traditional library of pre-recorded videos, QuantHub has built an AI bot named Chip that creates a fully curated learning journey for each individual user. This allows the learner to receive a hyper-tailored learning experience that provides just the right skills, at the right difficulty level, with the best and most relevant learning assets, all uniquely curated for each individual user at login.
Learning Journey Process:
- Select, define, and refine the individual’s current (or desired) role and the relevant data skills needed to succeed and advance
- QuantHub boasts the industry’s most comprehensive data skills taxonomy, identifying over 500 data skills across 6,000 items at a granular (and hierarchical) level
- Baseline and benchmark the individual, team, or class on current capabilities against the backdrop of skills needed
- Chip creates a custom learning journey based on each user’s baseline scores – focusing on skill gaps, and tailoring question difficulty to the user’s current proficiency
- Lessons are presented in micro-learning format (5-10 minutes per session) and are available on desktop, mobile device, and Microsoft Teams, using natural language chat instruction.
- Instead of using a static bank of resources, Chip draws from over 40,000 curated resources that change each week – pulling in the best, most current, and relevant content from across the internet. Administrators can optionally pull in premium (paid) content, or internally created content to be delivered seamlessly with other materials in the coursework.
- Once skills are mastered, they will periodically resurface as “review” items – all based on Chip’s forecasting when an individual is likely to forget a skill. By predicting the “forgetting curve,” Chip is able to increase long term retention and recall.
- Once users complete their selected learning level, they are eligible to receive certification.
- Teams and Teachers can use QuantHub’s gamification features like its User Scoreboard and Insights Dashboard to reveal user engagement and skill progress.
LEARNING PHILOSOPHY OVERVIEW
At QuantHub, we are always seeking to blend a robust combination of learning philosophies into our AI-powered platform. Our goal is to create the ultimate user-centric, long-term retention focused approach to individual upskilling across the data skills spectrum. Following are some of the major learning tools and philosophies we have weaved into our approach. Each learning tool description is followed by a summary of how our AI bot, Chip, delivers hyper-tailored learning for each user.
Spaced Repetition – Re-presenting topics at just the right time to interrupt forgetting. QuantHub uses the ebisu method for spaced repetition to schedule skill reviews when there is an 80% probability of the learner answering the question correctly. A recall probability of 80% has been shown to be the optimal time to interrupt the forgetting curve (known as desirable difficulty). When recall probability is higher than 80%, reviewing is a waste of time. When recall probability drops below 80%, the learner is at risk of having to completely relearn the skill.
Chip predicts when you are about to forget a skill and schedules a skill review at just the right time.
Interleaving – Mixing topics to promote “transfer learning” rather than the traditional approach of studying subjects class by class and chapter by chapter. QuantHub ends study activities after five responses and presents a different skill for the learner to study.
Chip tracks how long you’ve been studying and interrupts long study sessions by queueing up different skills to help you draw connections between different topics.
ADEPT method – Speeding up learning new skills by treating them as extensions of skills already learned (building on skills already learned). QuantHub uses prompts to ask a learner to reflect on what they already know about a topic before going into a study activity.
Chip builds on your current knowledge by relating new skills to what you already know.
Meta learning – Providing learners with a broader context of a topic or skill category to show how it fits or relates to other topics. QuantHub does this via our skill profile where you can see where the skill falls in the learning path, and in our skill detail page where a learner can see related skills.
Chip gives you context about a skill by showing you how the skill is related to other skills.
Varied practice – Mixing methods for learning. QuantHub curates resources in various media (e.g., video, articles, podcasts, etc.), presents assessment content using various item types (single select, multi-select, order, slider, etc.), and includes a variety of assessment types (Knowledge check, skill analysis, study activity, review, scavenger hunt).
Chip learns how you learn best and points you to learning content that maximizes your learning gains.
Active learning – Acquiring new information by being an active participant in the learning process. QuantHub motivates a learner to actively search for the answer to a question as opposed to passively watching or reading learning material.
Chip keeps you engaged in what you’re learning by asking you to find the answer to a question while you’re reading or watching learning material.
Active recall – Reinforcing skills learned through testing. QuantHub uses quizzes to measure whether a learner has gained proficiency in a skill. This not only updates our model of the learner, but also strengthens skill-related concepts in the learner’s mind known as the testing effect. Many think that they are learning when they are acquiring information, but recall is what shows the greatest change in brain activity and, therefore, is what cognitive psychologist believe is required for true learning.
Chip constantly measures your skill proficiency and retention by asking you to recall what you’ve learned about a topic.
Forward-testing – Testing before studying a skill to lay the foundation in memory for where the skill will live. QuantHub uses quizzes to trigger a nagging curiosity in the learner’s brain that can only be resolved when the learner finds the answer to the quiz questions. Cognitive psychologists have found that testing an unlearned topic causes the brain to create neuro-connections to where the information will live once it is learned.
Focus/diffuse – Mixing concentrated study with time to reflect. QuantHub facilitates this by locking skills when a learner loses all their lives and by interleaving topics (see Interleaving). Taking the learner out of focused study session allows time for their brain to keep churning on problems in the background.
Chip tracks how you are doing in a study activity and interrupts your session when it’s time to take a break from a challenging topic.
Top down/Whole game – Starting with the high-level objective and filling in the skills that are required to achieve it. QuantHub uses missions to simulate real-world data projects. QuantHub recommends skills to study based on how well a learner did on the mission. This is akin to playing a “game” and then practicing so that you can play better the next time. Not only does this provide context to help learners understand how they can apply their data skills, but it also leverages transfer of learning by practicing skills on real world tasks.
Chip presents you with real-world missions and creates a personalized training plan so you can work on skills that need improvement before jumping back in to tackle the next mission.
Low-stakes testing – Minimizing the risks associated with failing a test. QuantHub keeps the tone and feel of assessments as light and fun as possible. Taking tests is not a popular hobby for any (normal) person. QuantHub is an environment where getting a quiz question incorrect is celebrated as a found opportunity for learning something new. QuantHub takes the approach used by elite athletes who prioritize weaknesses to focus on when training.
Chip does not assume that you know anything when you take a quiz. In fact, Chip views incorrect answers as an opportunity to learn something new.
Layered feedback – Outcome, Informative, Corrective. Giving learners the right type of feedback at the right time. QuantHub provides outcome-level feedback to learners for summative assessments usually in the form of an overall score. QuantHub provides informative feedback for diagnostic assessments by pointing to specific skills and skillsets where a learner is strong or weak. QuantHub provides corrective feedback by pointing to learning resources where learners can study and understand how to correct skill gaps. Feedback is often delayed which has been shown to enhance learning.
Chip gives you regular feedback throughout your learning journey. This feedback comes in the form of overall assessment scores, skill-level scores, and question-level grading.
Reflection and elaboration – Providing time for learners to think about what they’ve learned and opportunities to internalize learning by relating topics to personal experiences. QuantHub presents learners with post-activity surveys that ask a learner to list three things they learned in their study session, two things they found interesting or unexpected, and one way they could apply what they learned to their lives.
Micro-learning in the flow of work – Bite-sized learning modules that mimic on-the-job-training. QuantHub is like a curated Google session where a learner is prompted with a query to look up and then provided with the most relevant resource to answer the query. Likewise, QuantHub provides learners with the Ask Chip search engine where they can go off-script to explore resources that answer questions on-demand.
Adaptive testing – Adjusting the difficulty of the material to meet the learner where they are at. QuantHub uses Bloom’s Taxonomy to assess across a range of complexity. Using computerized adaptive testing, QuantHub can precisely pinpoint a learner’s proficiency and present them with learning material that is at the level that best meets them where they are.
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