Learning Outcomes in Tamil


Learning outcomes are quantifiable statements of what students should be able to accomplish by taking a course, with practical outcomes being concise, meaningful, and clear statements of expected student abilities as a result of taking it.

For cognitive domain learning outcomes, it can be helpful to choose an action verb that ties directly into each level of Bloom’s taxonomy. For knowledge, for instance, this could include verbs such as identify, explain, or recall.


Learning outcomes are measurable skills, abilities, and values that students should be able to demonstrate as a result of participating in a course. Effective learning outcomes should be student-centric and clearly stated so students know what is expected of them in a system. Writing outcomes out clearly helps guide both teaching and assessment so the course reflects what will actually be evaluated rather than simply what an instructor thinks will make a good lesson plan.

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Understanding means being able to interpret and comprehend what someone else is communicating through words or gestures, which requires active listening skills – something highly esteemed in Tamil culture. Listening is considered a sign of respect and empathy while paying attention to nonverbal cues such as facial expressions, body language, and tone of voice is also of great value in understanding communication between two parties.

Tamil belongs to the Dravidian language family and features diglossia, or dual register usage depending on socioeconomic status: an upper register is used by upper-class people while working-class individuals use a lower record; these registers differ both phonologically and vocabulary-wise from each other.

Tamil dialects vary considerably in both lexical and grammatical features, reflecting geographical and historical factors that shaped them. For instance, Centamil (classical Tamil) uses “here”, while Kongu dialect of Coimbatore uses inku, Thanjavur/Palakkad/Sri Lanka dialects use inga and some even use kai!

Tamil, like other verbal languages, contains inflections that indicate person, number, mood, and tense by attaching suffixes to verb stems. Furthermore, any word may be negated by adding the word illai illa at the end of a sentence. Finally, Tamil is considered a null-subject language, which means that valid sentences may exist without subjects, verbs, or objects, such as in the example Ku Kodai veena naan.


Knowledge alone isn’t enough; students must also be able to apply it in new situations. Active verbs for this can include employing, illustrating, interpreting, practicing, and solving. Furthermore, analysis or synthesis can be added onto these active verbs in order to identify parts and their relationships as well as compare/contrast/criticize/experiment with them in new ways and put knowledge together into new combinations.

Learning outcomes should not only be measurable and realistic; stakeholders involved should also agree on them before creating them. This ensures that statements are clear without leaving students guessing what their instructor expects of them.

Trainers can ensure the courses and activities they create focus on relevant skills that will most benefit learners, which in turn will lead them to achieve their desired outcome. Furthermore, this gives trainers a measurable standard by which to judge how practical their training was; this enables them to identify any areas for improvement so as to provide more effective education, creating learner-centric programs where all activities aimed at giving learners all of the knowledge and tools needed for success is also an option.


Learning outcomes form the core of successful courses. They should be student-centric, measurable, concise, and meaningful, as well as outcome-based rather than task-based, written with language that learners understand, and used as a way of evaluating learners during courses as well as organizing them effectively.

Student learning outcomes typically represent what knowledge students should be able to demonstrate by the conclusion of a program of study. Faculty determine what students are expected to attain as a result of participating in such an endeavor, and these could include skills, attitudes, or behaviors.

An effective student learning outcome should be specific and use concrete action verbs corresponding with each level of Bloom’s Taxonomy of Learning Domains. For instance, one such effect might read as such: “Students will be able to accurately diagnose an automobile engine’s condition by performing various tests on its cylinder head. This can be measured through self-reporting by students as well as direct observation by instructors.”

By considering outcomes from the start, administrators can ensure their training focuses on providing learners with the knowledge and tools to be successful. Furthermore, this approach brings trainers closer to understanding what motivates learners, helping them design an engaging curriculum.


Learner-centric learning outcomes are at the core of successful courses and programs, providing a framework for course design that is student-centric, measurable, meaningful, and easily understandable by students. Verbs used to describe cognitive growth should be used when writing these learning outcomes as they should start with an action verb rather than tasks, and their focus should not shift away from consequences over functions.

Establishing clear, observable, and measurable learning outcomes allows you to align course content, assignments, assessments, and evaluations with desired learning. This ensures your course remains relevant for students while helping them understand why certain material is essential – an invaluable way to evaluate the effectiveness of training programs!

Researchers conducting research in Tamil employed a text-to-image generative network and discriminator model to create an image synthesis architecture. The text-to-image network made vector representations from text input; the discriminator used these vectors to synthesize images. As a result, the image synthesis model boasted strong performance. Furthermore, researchers also added a Hybrid Super Resolution GAN (HSRGAN) model in order to optimize the performance of language and image synthesis architectures.

Synthesis architectures can help bridge the divide between research ideas in regional languages and universal ones. Most generative models for text-to-image synthesis use universal languages as training data, however the HSRGAN model stands out by performing equally well when applied to both universal and Tamil languages, making it an attractive candidate for future applications in regional language applications. Furthermore, its performance exceeds that of other models on two demanding datasets due to taking advantage of Tamil’s rich morphology for generating high-resolution images from text input.