Assessments have become integral to today's teaching, learning, and data-driven decisions and help in structuring the learning paths to mastery the concept.

Assessments can be broadly classified based on the usage as

  • Assessment for Learning
  • Assessment as Learning
  • Assessment of Learning

Assessments can be used by Faculty and Systems (LMS) to help the student in their learning journey.

Assessments for Learning

The primary objectives of these assessments are

  1. Help learner revise concept using remedial assessment
  2. Identify the understanding depth at the early level and take corrective actions
  3. Monitor the progress and adapt the learning paths to fit each student need.
  4. Track the effectiveness of their intervention and instruction

The different types of assessments are

  1. Remedial/Practice Assessments: Assessments are given for each concept where answers and solutions in the form of videos and notes are given with each question. It should help learner to re-cap and understand the concepts.
  2. Formative Assessments: Assessments are given for each concept or chapter where student progress is monitored, corrective actions are implemented. Corrective actions can be system or faculty driven. In case of system, Adaptive Learning paths are generated to meet the student understanding depth and level. In case of Faculty driven, faculty can suggest corrective actions based on each student need with additional worksheets, tests and reading material. Faculty driven becomes tedious at scale and human dependent. System generated Adaptive Learning are very effective with AI/ML where the system can fine tune exactly based on the learner needs.
  3. Gamified Assessments: These are remedial assessments packaged as Games to make it interactive and interesting. Level based journey in the game can be used to take the learner from basic concepts to advanced concepts with each level making the user learn concepts at different depths.

Assessment as Learning

Assessments when viewed as collaborative and community led, are called Assessments as Learning. In this mode of assessment, students share the responsibility of assessing not just their own work, but also that of their peers. It helps in learning from each other and using the feedback as a tool to improve the teaching-learning process. These types of assessments include:

  1. Peer Reviews: Students review the work or tasks of other students and provide feedback based on a rubric. This enables them to critically analyze other perspectives and compare them with their own perspectives, to promote better learning.
  2. Collaborative Assessment: Students can assess a particular idea or topic from multiple perspectives and evaluate alternative solutions to problems. In the process they discover new and more advanced knowledge.

Assessment of Learning

The primary objectives of these assessments are

  • Aimed at assessing the progress as per the required goal across larger groups
  • Measure success at the end of the checkpoint and results in certificates or moving to higher grade/level

 The different types of assessments are

  1. Interim Assessments: Assessments are given across larger groups in a periodic manner to gauge the progress across larger group of learners. It helps students to set goals and works on the areas that needs attention.
  2. Summative Assessments: Assessments are given across larger groups to understand or evaluate the learning at the end of the check point or curricular area (criterion-referenced assessments). It would help to determine the learner grade and move the learner to next level.
  3. Benchmark Assessments: These are assessments that are usually norm-referenced – i.e., they benchmark a student against others.

Assessment Technologies

Assessment technologies today are very advanced. Various types of assessments are created within a Question Bank and Exam Management System. The questions are tagged to difficulty levels, Bloom’s taxonomy (a codification of cognitive skills), ideal time required to solve and other parameters. The exams can also be configured to be linear or randomized, time bound and with defined schedules. The evaluation mechanisms include automated scoring of many types of questions and scoring or grading by a teacher. Once, the Learning Management System (LMS) captures lot of data on the learner responses. It then tabulates, analyzes, and presents this data, and generates specific recommendations for the learner to use in order to improve learning. Systems can use artificial intelligence for the presentation of the exam questions. Systems get a better picture of student capabilities   and the way the   attempts are done. Such systems are also able to orchestrate the teaching – learning interaction with the learner in a dynamic manner, resulting in personalized adaptive learning to address the unique needs of each learner.

Many Learning Management Systems also employ gamification in various assessments to engage the learner better and motivate the learner to perform better. Gamification involves using game-based elements such as points, badges, leaderboards, streaks, increasing levels of challenge, and peer competition or teamwork on tasks. Using principles of behavioral sciences, learners are given nudges to perform incrementally better (reinforcing good behavior and performance). They are also encouraged to maintain their “streak” – a term given to a sequence of consecutive good achievements.


The goal of various assessments is to improve education at the different stages of student learning cycle. It can be a very effective learning tool when implemented properly to aid the student in the learning journey. AI based adaptive learning will be the future, where learning paths are fine tuned to each individual, which in-turn help improve the learning experience at scale across diverse economic communities.

About Author

Seethaprasad Mandikel is the founder and CEO of TriByte Technologies, a Bangalore (India) headquartered  company, focused on developing a cutting-edge Interactive Learning Platform.

Seethaprasad comes with 25 years of experience in building technologies for large scale deployment with last 10 years focused on EduTech. He has grown TriByte from start-up to enterprise by providing white-labelled solutions for many marque customers serving millions of users in 7 countries and 3 continents.

Prior to TriByte, he was part of founding team of PI Corporation (later acquired by EMC) as Development Director. He has also key roles in companies like Microsoft Corporation (in US), Talisma/ Aditi Technologies as well as Robert Bosch India.

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