Home / EdTech

Blended Learning with DeepGrade

Ever since the pandemic outbreak happened almost the entire world made a shift to digital functioning on a daily basis. Educational institutions are at the spotlight to create quality platforms to continue learning at any situation.

With the widespread impact of covid, upholding the peak learning hours of students becomes a massive task for the educators today. This led to the brainstorm on how to catch up this crucial time and also not to affect the mental health of students.

In the upcoming days, It is very much impending that the schools and colleges have to work on the remodeled hours of institutional functions. Active learning hour is the next measuring indicator for educators when balancing between the in-school learning and home learning time. The structure of conventional methods are the first to be affected due to this disruption such as classroom teaching, class notes , paperback assignments, assessment methodologies and uninterrupted monitoring of students progress.

Though the online live classes tried to fill up the void, there are difficulties to gather everyone at the same time, internet fluctuations, missing out lessons, and unorganised follow ups, etc. It is high time that we need to focus on remedial modelling in par with the remodelling education system. Needless to say that edtech have curved the solution to support the chalk talk school modelling to see a new swift solution driven by technology.

DeepGrade is designed to be the flexible platform that serves as a one stop solution for all learning needs of an educational institution. Such remedial features include

Adopting technology is the strategy for educational institutions to seize the learning gap and also an opportunity to strike high end technology like AI into their premises.Deepgrade creates a holistic learning platform with the help of Artificial intelligence that further adds to its potential in the areas of innovation and teaching practices being able to learn and evolve quickly.

Though the pandemic remodelled the mode of education all over the world. it is evident that this blended learning format accelerated the necessity in schools and colleges to let the learning continue at any situation. Herewith we are proud to introduce our solution Deepgrade in the forefront of its adoption and geared up to support in empowering the educators.

AI trends, EdTech

Digital Upskilling for Teachers

EdTech | AI Transformation | Teaching the Teachers

According to ZipRecruiter’s (a SaaS based job search engine) future of work report, one of the key findings is that employers across several industries have begun offering more on-the-job training to prepare for tomorrow’s job market. AI is creating more jobs and will continue to create new roles and new jobs for everyone in every industry. Countries such as UK has been facing issues with digital skill gaps for few years now and it is known fact that there is impact seen on the business and economy and one of the measures taken was towards fixing the digital skill gap in educators.

Education system is one of the potential domains where both student and teachers need to embrace digital skills. Its foremost important that teachers need to be exposed and upskilled as a first step. If we promote the culture of teaching the teachers then they can be inspiration to students, be a role model to teach the student community.

In this startup era, we see innovation interms of various forms of training/assisting products whose major focus is to upskill the student and make student life easier. However, there are handful of products which focuses on educators in general.

Our objective is to bring out the need for digital upskilling for teachers and aspects that can be motivating the educators to come forward and take up digital upskilling initiatives. To add further McKinsey report has indicated that 20-50 million new jobs will be created in the technology sector by 2030.

The primary driver for the need of digital upskill is the changing landscape of technology itself and lack of adoption of the technology or latest trends in education system. There have been trends of adopting the collaboration, communication tools and applications. However clearly there is reluctance in general seen in adopting the AI based solutions towards automation, tutoring and many more.

There are several aspects that can serve as motivation to embrace digital upskill among educators. Listing down some of the most key aspects.

Awareness is one of the important aspects. Institutions must take steps and plan towards creating awareness among its staffs, teachers on the changing environment in tech industry and how it can bring impact to the individual level if upskill is not done. Teachers must attend industry led conferences and start writing blogs and articles to circulate among the educator’s community on the trends and enable awareness which will be also used by the student community.

Continuous Learning & Training Platforms are mandatory and not an option anymore. The one-time sessions are not going to solve the larger issues at hand. There is a need to have a continuous learning, training, certification and upskilling platforms which can provide in depth topics (granular level) with real time examples from the subject matter experts in the field with measurement models.

Technology & Data is the king in the era of AI. Educators/Institutions needs to be aware the data they have in hand and the role of technology in curating and providing very meaningful insights which can be used for various purpose, which is limitless. One of the purposes would be to help bridge the skill gaps in educators to large extent.

Automation such as process or workflow which are time consuming needs to be identified and automated with the help of AI driven software and tools. This initiative will help save lot of time, effort and energy which can be redirected towards upskilling by using the continuous learning and training platforms.

Collaboration with industry, within educators are critical for the success of digital upskill plans. Industry connect on regular basis, partnering with industry pioneers, startups who are exploring new technology is key. Setting up joint Artificial Intelligence research lab will be a great move. Colleges/University who have vision towards latest trends and technology have been adopting this for some time now, What about schools and colleges in tier 2/3? It’s time to embrace the trends in large scale to reap the benefits that AI transformation will bring in.

Simulation models can be very useful in helping not just students but educators as well, it’s time to apply what is being learned and taught as well. Partnering with companies which are transforming the learning experience through the use of simulation models will be a great initiative.

Finally, Strategy will be key, Institutions needs to have a clear strategy towards all emerging trends not just AI, need to come out with a clear plan on how they are going to strategize, plan, budgeted, adopt solutions and measurement metrics in order to ensure that core objective of digital skill gap gets addressed time to time.



 iGen (also called the Generation Z and Generation Alpha) are considered to be the most fortunate and highly knowledgeable among the recent generations in time. The ratio of these two makes the highest population in the world who will be redefining the future with Technology.  

Gen Z and Gen Alpha are those born between 1990 – 2010 and 2010 – present.  At this time, people born in this classification form the young workforce, higher education practitioners, and school students.  They are privileged with access to opportunities which no previous generations have enjoyed.

How Streamlined Education systems respond to this revolution?

As an intervention, the demand in the education system is a more inclusive, efficient, and accessible environment in their teaching and learning methods. Parents of this generation have more expectations for the educational institutions to provide a holistic development of their children to master as a Global citizen and also as a better human at the same time.

Hence creating Digital boards and video learning materials is not enough.

In light of the existing initiatives, Artificial Intelligence enters into a scene like a superstar in providing intelligent data processing for educational institutions and optimizing personalized teaching and learning methods.

Here are some ways that arise with the advent of Artificial Intelligence and the foreseen modification expected in the mainstream education system.

  1.  Personalized Education:

Why does it have to be personalized? Why not one size fit for all? This is because the Next generation is a flock under the tree learning to reach different heights and distances.

Bringing the big data in front, understanding every student based on their previous memory and their ability in learning is impossible without AI.  

We wonder how ad recommendations pop up based on our previous search details, it is the same how each classroom based on majority / each student in class will be recommended with topics, methods of learning and assignment options. Personalized learning will provide customized feedback for the improvement of teachers and students with no bias, ensuring students get the best possible assistance.

2. Automated basic functions like Assessment Tool:

Transforming the traditional practices in the grading system, Artificial Intelligence tailors the adaptive testing tool based on the individual interest and knowledge level. A student’s response is the key for the Deep Grade process which helps to unleash the quantity and quality of individual learner.  

3.  Tutoring Assistance – An additional support:

It is obvious a machine cannot offer everything that a teacher provides in the class with all their limitations in front of them. It is also a vice verse that the AI-powered tutoring assistant can teach through their binary numbers to cross the boundaries of time and space. At the age when parents find it harder to answer the toddler’s question, a Sensible Machine will provide information to satisfy the curiosity to question.

4.  Reality Classrooms: 

Though conventional learning process is not the phase of iGen but the digital classrooms are also becoming old concepts to them. Now Augmented reality (AR) and Virtual reality (VR) is the new blooming concept and highly expected even in remote regions of the education industry. 

Just imagine a big whale swimming through the classroom then suddenly paused and facts about the whale will be explained to the students. The real  time  experience using Virtual Reality and Augmented reality establishes a new level of experiential learning. We will be able to experience forests, agricultural fields, historical situations, space, parliament houses, Math formulas, etc inside the classroom. 

5.  Altered perspective towards the interaction with Data:

This technical intelligence is providing the iGen a different lens to look at the data with not just a Bird’s eye view but also with the active and passive learning capabilities in it. It plays a big role in how they interact with the data and filling the loopholes to complete the void.

Students in the future may have vastly different experiences and perspectives with applicable knowledge, skills, and accessibility to facts than the previous generations.

6. Inclusive Education Modules:

Education based technologies such as Assistive teaching and learning programs with AI in education created more accessibility and inclusive for every learner. This system is a boon for rural education, differently able students to progress with their fullest potential.  These AI-enabled programs also empower both teachers and students to upgrade themselves with current trends.  


In order to harness that immense potential, it is important to have a clear understanding of the inner workings of Artificial Intelligence and the eloquence of the solutions it provides. 

Photo by Agence Olloweb on Unsplash
AI trends, EdTech

Sharpening Taxonomy

The Era of AI

We are not living in the changing world of AI, instead we are living in the world where using AI we are enhancing the experience and productivity. This article has got its inspiration from the same thought by enhancing the well-known education framework “Bloom’s Taxonomy” to suit the needs of changing world of AI. Sharpening Taxonomy – the era of AI is indeed a experimental work towards enhancing the blooms taxonomy to reflect the higher order skills needed for the AI industry beyond 2020. Our intended target audience are academicians, institutions, future focused schools, students, industry professionals and AI researchers/SME’s.

Bloom’s taxonomy is a set of three hierarchical models used to classify educational learning objectives into levels of complexity and specificity. The three lists cover the learning objectives in cognitive, affective and sensory domains. The cognitive domain list has been the primary focus of most traditional education and is frequently used to structure curriculum learning objectives, assessments and activities. Source

Cognitive skills in general is defined as the ability of an individual to perform the various mental activities most closely associated with learning and problem solving. Source Wikipedia. In simple terms it’s the core skills our brain uses to think, read, learn, remember, reason, and pay attention. Working together, they take incoming information and move it into the bank of knowledge you use every day at school, at work, and in life. While the purpose of bloom’s taxonomy is to help as an education framework to setup learning contents, assessments in schools and colleges.

This novel work is to enhance the taxonomy by adding additional attribute to the framework to suit the needs of AI world which will be faced by student community in their post school/college roles. The cognitive skills are setting the base and does get extended in our professional work and life as well as individuals enter into career path. Therefore it has become essential to look at the attribute needed to survive in the industry today and start practicing the same to balance the work life in this AI world.

Below is a representation of Blooms’ Taxonomy commonly found. In addition, Below has the revised visualization which represents broad wedge at the top highlights the value of creating, evaluating and analysing in a better way giving the importance of all.

Bloom's Taxonomy

In short, the lower levels of Bloom’s taxonomy focus on the knowledge that we want our students to acquire – what we want our students to remember and understand. The middle levels focus on application and analysis of information. At the top of Bloom’s taxonomy are tasks that involve creating and evaluating. Source

If we look at what Industry wants, it expects its professionals at work to possess the same skills that are laid out in the above framework. Such as remembering, understanding, applying and other higher order skills as a part of day to day work. Higher values of creating, evaluating, analysing are no longer a value additions, they are considered as regular expected outcome that a professional brings in his or her work. So what defines one’s success in his or her career path is a question that is in the mind of professionals at large.

AI based solutions are gaining more traction and organizations, people have started adopting them, the adoption percentage is expected to increase as we move towards 2021 onwards. It’s the need of the hour to look at attributes that are needed for today’s industry, There is no single attribute for success, it’s a collection of various attributes. However for the purpose of the discussion here, we are going to discuss and talk about only one attribute “Fluidity” which is key for this era of AI.

It’s known fact that if one needs to achieve greater level of success in career he or she needs to break barriers and boundaries and move out of comfort zone irrespective of the field of work.

Being with Fluid mind, helps to deal with uncertainties, dynamic change of course or action, changing technology, new skills, continuous learning, adapting to changing environments. Its therefore a person with multi-disciplinary skills has a upper edge. It is due to these reasons we are adding fluidity as a key attribute in the higher order skills of Taxonomy.

A screenshot of a cell phone

Description automatically generated

The above represents the bloom’s taxonomy and the modified taxonomy with added attribute (FLUIDITY). Students, academicians, industry professionals need to be aware of the changing environment and embrace the additional key attributes time to time to enhance and sustain one’s skills in order to succeed in the era of AI.


Transform Hodgepodge grading

EdTech | Artificial Intelligence | Machinelearning

One of the unexplored areas in EdTech is grading. Grading is expected to be the basis for some of the Key AI based transformational usecases. One of the recent survey states that $6 Billion is the estimated total market value of AI in education by 2024. That is not too far, and AI is becoming increasingly part of education industry. Educational institutions are in a race to catch up the trend and welcome the change AI is bringing to entire education industry.

According to, not only is education being transformed as far as science, technology, engineering, and math (STEM) curricula, but the education industry as a whole is being transformed by AI. Increasingly, educational institutions from elementary to higher education as well as adult and professional learning are being transformed by intelligent systems that are helping humans learn better and achieve their learning objectives.

What is hodgepodge Grading all about? Educational institutions today have aligned themselves towards standardized curriculums and assessments. What about grading practices? A question that is being asked time to time. Though grading is a key responsibility there are challenges of determining which academic vs non-academic factors representing student’s achievement.

According to, Teachers often use “hodgepodge grading,” i.e., a combination of achievement, effort, growth, attitude or class conduct, homework, and class participation. A survey of over 8500 middle and high school students in Virginia supported the hodgepodge practices commonly used by their teachers (Cross & Frary, 1999). According to Virginia Commonwealth University (VCU) 2011 grading related theses, measurement theory experts recommend that achievement factors should be the only factors that determine student grades, the results of the study conducted indicates that teachers use a mixture of variables in determining student grades, known as hodgepodge grading.

The purpose of this article is to leave the readers with thoughts of goodness that AI and Machinelearning will bring to education industry and includes grading practices as well, which calls for transforming traditional way of grading for sure. This is an initiative towards visualizing a future where innovative ways are being used for grading and AI/ML is one among them…


  • AI based grading will clearly communicate the process, method in which grading is being done to student and teachers’ community. This is one of eight recommendations made by measurement experts in terms of grading practices that were found to be consistent in various survey.

  • AI based grading does not consider students attitude, levels of interest in the subject and personalities. This is again one of the eight recommendations made by measurement experts as well.
  • AI based grading considers written form of tests as primary means of measuring achievement and However it also provides flexibility to configure measurement parameters which can be academic and non-academic aspects to certain levels.
  • AI based grading system provides teachers with the huge insights and very useful feedback to them which is possible with the machinelearning techniques.
  • AI based grading system provides teachers on the focus areas per class/per student/per subject without much effort using the data where they get meaningful analytics which can be used for upskilling teachers themselves in certain areas and when followed will provide a different level of high-end results.
  • Students and teacher’s performance assessments can include AI based grading results.
  • AI based system does not consider the fact that achievement should be the sole ingredient in determining grades. The system does help and provide useful configurable parameters or measurements which can be used to get overall achievement factors and not just grades.
  • The grading systems can adopt to new policies.
  • Using the AI based system one can be rest assured that amount of grading data gathered over time will be a great asset to reflect enough evidence on performance of each student.

Finally, there are in total 3 key recommendations which we found interesting in the VCU theses, which we refer here for the readers benefits.

Recommendation A: States that research is needed to determine why a disparity exists between grading recommendations and practitioners and to determine how to narrow the gap. Teachers do not solely rely on student achievement grades and they also rely on a variety of factors. It may be seen that grading recommendations followed today do not relate to the practical use in the classroom.

Recommendation B:  States that further research is needed in the area of how to report grades that serve as a multipurpose tool which includes both academic and non-academic factors. Developing a student reporting method that produces a comprehensive picture about student performance, ranging from achievement grades to student behaviours, such as effort, motivation, and responsibility, may alleviate the teachers’ need to average student achievement grades with varying non-achievement factors. This may provide big picture to students, parents, and teachers about what a student grades represent.

Recommendation C:  States that future research to determine the effectiveness of teacher training or professional development opportunities to help teachers meet the challenge of grading.

 In conclusion quoting from Zoeckler, 2007 For grades to be interpreted with accurate understanding, the grade requires an understanding from both the student receiving the grade and the teacher assigning the grade. AI based grading can further provide the solution to the above recommendations and transform Hodgepodge grading defining a new age of grading.

In the not too distant future, you can expect that AI and machine learning will be a core part of all educational experiences. AI is starting to show its benefits and application to a wide range of educational needs, and the hope is that it will greatly improve overall learning outcomes for all –


Company, EdTech

Solving Biased AI using DeepGrade

EdTech | Machinelearning | DeepGrade

The objective of this article is to provide readers on how technology [such as machinelearning], AI based solutions [For Example: AI-powered assessment and learning system] and Data are inter-related and how problems such as Biased AI can be solved or avoided in the context of adopting AI in Education.

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data“, in order to make predictions or decisions without being explicitly programmed to perform the task. Source –

Machine learning is all about learning from Data and machine finds patterns from the data. ML is all about Data and Algorithms, but Data is key. ML offers various model, but it depends on the volume of data one has. Based on the volume of data simple to complex models can be chosen.

Adopting AI-powered EdTech solutions are on rise and will play a major role in next few years, lot of new innovations are on the way towards how to improve AI in EdTech. While this growth and transformation is good for the education industry there is a need to address several factors as they come in. We are going to touch upon one such factor which is Biased AI and how this can be solved.

EdTech platforms and solutions are on the rise and examples to name a few, AI-powered assessment and learning system, AI tutoring, Personalized learning, Experimental learning and AI assisted education or AI led education. Companies globally (countries US, Europe, China and India) are all investing heavily in EdTech. Analysts report that AI usage in U.S. education will grow 47.77% from 2018-2022. The EdTech platforms are going to rely on the data as one of the key source of input to be fed into their machinelearning system. There is a known problem here..  AI systems learn and amplify human biases the problem is when an AI-powered EdTech system was trained on data annotated by humans, the humans’ biases tainted the data, which in turn infected the algorithms, which in turn produced biased outcomes. We call this as Biased AI.  

There are two types of bias in artificial intelligence and machine learning: algorithmic/data bias and societal bias. Our focus is more towards algorithmic/data bias (training the AI with biased data) alone for this article. The data bias differs from product to product. Let us take an example of AI based assessment and learning system and see how this can be solved by another AI based solution such as DeepGrade. Consider applying a AI based assessment and learning system which tries to consider the assessment reports which are manually evaluated (which is the process in today’s educational system majorly) to be fed as a input data into machinelearning algorithms. Then there is high possibility of getting Biased AI reports. Primary reason for biasing is varying patterns of manual evaluation when fed into machinelearning system then it tends to learn from errored data and tends to generate biased report.

The AI Bias can be explained with various examples, above is very simple example considering evaluation or grading as a source where the data gets generated which is given as input to machinelearning to generate models.

AI based grading/evaluation solution such as DeepGrade comprising machine learning algorithms can add value to the overall grading or evaluation practice, they could not just replace hodgepodge grading practices but avoid biased AI practice as well.

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from Youtube
Consent to display content from Vimeo
Google Maps
Consent to display content from Google
Consent to display content from Spotify
Sound Cloud
Consent to display content from Sound