Education Technology Services
How edutech uses ML to create a personalized educational experience
EduTech, or educational technology, has been leveraging machine learning (ML) techniques to create more personalised educational experiences for learners of all ages. ML algorithms can analyse large quantities of data to identify learning behaviours, patterns, and preferences, predict learning outcomes and even plan and recommend curriculums. Such insights enable teachers and other knowledge facilitators to gauge learners’ depth of perception more clearly than traditional teaching methods allow. Based on all the insights available, educational content can be customised to suit individual learners.
Here are more details about ML in edutech.
- Adaptive learning platforms
- Predictive analytics
- Intelligent tutoring and recommender systems
- Natural Language Processing (NLP)
- ML boosts efficiency
ML algorithms are used to track learners’ progress, understand their strengths and weaknesses, followed by personalised recommendations and content. The pace of learning and difficulty levels can all be adapted to individual needs and as learners engage with the learning platform, ML algorithms continuously analyse progress and make real-time adjustments as needed.
ML models can be used to analyse learner data such as attendance records, academic performance, social interactions and sometimes demographic information, to identify potential academic struggles. Once detected, ML platforms can provide timely interventions such as customised support and additional resources to overcome learning handicaps. At certain learning levels, such an approach can help keep learners in the fold instead of allowing them to give up. In fact, classroom behaviour and facial expressions can also be tracked and used to identify learning gaps. Such a degree of personalisation allows for deeper engagement and focus leading to better retention of knowledge.
Personalised learning can prove to be especially effective for differently-abled and slow learners. The pace of learning can be controlled such that there is no loss in gathering knowledge.
Smart tutors are making a mark in the educational sector. These are ML-powered intelligent tutoring systems that simulate human tutoring and provide personalised feedback, assistance and guidance by analysing learner data, performance and interaction patterns, among other factors. Knowledge gaps, preferences, learning styles can all be identified and learners can be provided with insights into areas that need improvement. ML algorithms can also promote self-directed learning and recommend relevant learning sources such as books, courses, videos and other learning aids.
NLP techniques are a subset of ML and can be used to analyse and understand written or spoken language. NLP is frequently leveraged by edutech platform solutions to automatically grade essays and language proficiency assessments, and provide personalised feedback on written assignments. Learner responses to open-ended questions can also be analysed by NLP algorithms and responses and suggestions can be generated depending on individual needs.
ML effectively bridges the gap between teachers and learners. Automated systems connect both parties easily and enable teachers and facilitators to focus on the transfer of knowledge and thereby boost efficiency, instead of having to manage detailed information about the learners. For example, some of the most popular learning applications today use ML to analyse user data and personalise the learning experience, they are equipped with mobile notes and reminders, and video lectures are available on demand along with their transcripts. Learners can focus on relevant content without getting distracted by irrelevant content.
It is clear that AI and ML have together taken the edutech industry to a whole new level. While traditional approaches to managing educational operations still hold sway in many regions of the world, automated platforms driven by cutting-edge technology are making huge inroads and making the almost unimaginable possible. What was once considered the future has already arrived. In fact, industry reports indicate that the global education software market is expected to reach USD 11.6 billion by 2025.
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