daniyasiddiquiEditor’s Choice
Asked: 27/12/2025In:

fraudulent health cards in Lucknow to ...

fake health benefithealth card fraudhealth system exploitationinsurance fraudlucknow fraud casemedical scam
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. Selling Personal and Demographic Data One of the main reasons this fraud was able to succeed is because of unauthorized access to Aadhaar and demographic details. The accused allegedly collected personal details of individuals, at times without their knowledge, through middlemen, local agents, orRead more

    1. Selling Personal and Demographic Data

    One of the main reasons this fraud was able to succeed is because of unauthorized access to Aadhaar and demographic details. The accused allegedly collected personal details of individuals, at times without their knowledge, through middlemen, local agents, or informal networks that worked by exchanging information. In some instances, beneficiaries were deceived under false pretensions into providing documents in order to receive government benefits or signing up for a particular scheme.

    2. Enrollment and Verification Gaps Exploitation

    Most of the health schemes nowadays depend on a digital enrollment system, but verifications in most cases are semi-automated. The accused got away with fraud in areas where either physical verification was weak or hurried, or where there were a very large number of enrollments. In such cases, they would manipulate documents and upload them or re-use genuine data to create health cards that passed the system’s verification.

    3. Collusion and Insider Knowledge

    Frauds involving such processes rarely succeed without insider knowledge. The arrested individuals reportedly knew about backend processes, like how the applications move from submission to approval. This helped them in bypassing some red flags, delaying scrutinies, or submitting them in batches so as not to be noticed.

    4. Utilization of Nominee or Proxy Beneficiaries

    In many cases, fictitious identities or proxy beneficiaries were created. Such cards were then utilized at empanelled hospitals for raising claims for treatments that never took place. At times, genuine patients were shown procedures they never received, while in other cases, entirely fictitious admissions were created in the system.

    5. Poor Real-time Claim Monitoring

    Although claims are recorded electronically, there is no uniform use of real-time analytics or anomaly detection. This enabled the suspicious patterns, like repeated claims from the same facilities or unusually high-value treatments, to go undetected until law enforcement stepped in to take action.

    6. Lack of Beneficiary Awareness

    Most of the genuine beneficiaries are unaware as to how and when their health cards are used. The absence of instant alerts-through SMS or apps-means fraudulent usage of their identity did not raise immediate alarms. This delayed complaints and the perpetuation of fraud.

    7. Reactive Rather Than Preventive Controls

    This racket was brought to light through intelligence inputs and focused investigations, rather than automatic alerts from the systems. This highlights the fact that while the systems exist, their enforcement becomes reactive in most cases—post-financial leakage, rather than upfront.

    Broader Takeaway

    This certain incident has underlined that digital governance is only as strong as the weakest point of control. While technology allows scale and speed, it has to be duly supported by strong audits, beneficiary communication, periodic verification, and strict accountability. The arrests in Lucknow also point out that corrective steps and warnings go hand in hand with continuous strengthening of the system to protect the public welfare funds.

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daniyasiddiquiEditor’s Choice
Asked: 26/12/2025In:

generative AI models

artificial intelligencedeep learningfine-tuningmachine learningpre-trainingtransfer learning
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Understanding the Two Model Types in Simple Terms Both generative and predictive AI models learn from data at the core. However, they are built for very different purposes. Generative AI models are designed to create content that had not existed prior to its creation. Predictive models are designedRead more

    Understanding the Two Model Types in Simple Terms

    Both generative and predictive AI models learn from data at the core. However, they are built for very different purposes.

    • Generative AI models are designed to create content that had not existed prior to its creation.
    • Predictive models are designed to forecast or classify outcomes based on existing data.

    Another simpler way of looking at this is:

    • Generative models generate something new.
    • Predictive models make decisions or estimates by deciding to do something or estimating something.

    What are Generative AI models?

    Generative AI models learn from the underlying patterns, structure, and relationships in data to produce realistic new outputs that resemble the data they have learned from.

    Instead of answering “What is likely to happen?”, they answer:

    • “What could be made possible?
    • What would be a realistic answer?
    • “How can I complete or extend this input?

    These models synthesize completely new information rather than simply retrieve already existing pieces.

    Common Examples of Generative AI

    • Text Generations and Conversational AI
    • Image and Video creation
    • Music and audio synthesis
    • Code generation
    • Document summarization, rewriting

    When you ask an AI to write an email for you, design a rough idea of the logo, or draft code, you are basically working with a generative model.

    What is Predictive Modeling?

    Predictive models rely on the analysis of available data to forecast an outcome or classification. They are trained on recognizing patterns that will generate a particular outcome.

    They are targeted at accuracy, consistency, and reliability, rather than creativity.

    Predictive models generally answer such questions as:

    • “Will this customer churn?”
    • Q: “Is this transaction fraudulent?
    • “What will sales be next month?”
    • “Does this image contain a tumor?”

    They do not create new content, but assess and decide based on learned correlations.

    Key Differences Explained Succinctly

    1. Output Type

    Generative models create new text, images, audio, or code. Predictive models output a label, score, probability, or numeric value.

    2. Aim

    Generative models aim at modeling the distribution of data and generating realistic samples. Predictive models aim at optimizing decision accuracy for a well-defined target.

    3. Creativity vs Precision

    Generative AI embraces variability and diversity, while predictive models are all about precision, reproducibility, and quantifiable performance.

    4. Assessment

    Evaluations of generative models are often subjective in nature-quality, coherence, usefulness-whereas predictive models are objectively evaluated using accuracy, precision, recall, and error rates.

    A Practical Example

    Let’s consider a sample insurance company.

    A generative model is able to:

    • Create draft summaries of claims
    • Generate customer responses
    • Explain policy details in plain language

    A predictive model can:

    • Predict claim fraud probability
    • Estimate claim settlement amounts
    • Risk classification of claims

    Both models use data, but they serve entirely different functions.

    How the Training Approach Differs

    • The generative models learn by trying to reconstruct data-sometimes instances of data, like an image, or parts of data, like the next word in a sentence.
    • Predictive models learn by mapping input features to a known output: predict yes/no, high/medium/low risk, or numeric value.
    • This difference in training objectives leads to very different behaviours in real-world systems.

    Why Generative AI is getting more attention

    Generative AI has gained much attention because it:

    • Allows for natural human–computer interaction
    • Automates content-heavy workflows
    • Creative, design, and communication support
    • Acts as an intelligence layer that is flexible across many tasks

    However, generative AI is mostly combined with predictive models that will make sure control, validation, and decision-making are in place.

    When Predictive Models Are Still Essential

    Predictive models remain fundamental when:

    • Decisions carry financial, legal, or medical consequences.
    • Outputs should be explainable and auditable.
    • It should operate consistently and deterministically.

    Compliance is strictly regulated. In many mature systems, generative models support humans, while predictive models make or confirm final decisions.

    Summary

    The end The generative AI models focus on the creation of new and meaningful content, while predictive models focus on outcome forecasting and decision-making. Generative models will bring flexibility and creativity, while predictive models will bring precision and reliability. Together, they provide the backbone of contemporary AI-driven systems, balancing innovation with control.

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daniyasiddiquiEditor’s Choice
Asked: 26/12/2025In:

pre-training vs fine-tuning

artificial intelligencedeep learningfine-tuningmachine learningpre-trainingtransfer learning
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    “The Big Picture: Why Two Training Stages Exist” Nowadays, training of AI models is not done in one step. In most cases, two phases of learning take place. These two phases of learning are known as pre-training and fine-tuning. Both phases have different objectives. One can consider pre-training toRead more

    “The Big Picture: Why Two Training Stages Exist”

    Nowadays, training of AI models is not done in one step. In most cases, two phases of learning take place. These two phases of learning are known as pre-training and fine-tuning. Both phases have different objectives.

    One can consider pre-training to be general education, and fine-tuning to be job-specific training.

    Definition of Pre-Training

    This is the first and most computationally expensive phase of an AI system’s life cycle. In this phase, the system is trained on very large and diverse datasets so that it can infer general patterns about the world from them.

    For language models, it would mean learning:

    • Grammar and sentence structure
    • Lexical meaning relationships
    • Common facts

    Conversations and directions typically follow this pattern:

    Significantly, during pre-training, the training of the model does not focus on solving a particular task. Rather, it trains the model to predict either missing values or next values, such as the next word in an utterance, and in doing so, it acquires a general idea of language or data.

    This stage may require:

    • Large datasets (Terabytes of Data)
    • Strong GPUs or TPUs
    • Weeks or months of training time

    After the pre-training process, the result will be a general-purpose foundation model.

    Definition of Fine-Tuning

    Fine-tuning takes place after a pre-training process, aiming at adjusting a general model to a particular task, field, or behavior.

    Instead of having to learn from scratch, the model can begin with all of its pre-trained knowledge and then fine-tune its internal parameters ever so slightly using a far smaller dataset.

    • Fine-tuning is performed in
    • Enhance accuracy for a specific task
    • Assist alignment of the model’s output with business and ethical imperatives
    • Train for domain-specific language (medical, legal, financial, etc.)
    • Control tone, format, and/or response type

    For instance, a universal language understanding model may be trained to:

    • Answer medical questions more safely
    • Claims classification
    • Aid developers with code
    • Follow organizational policies

    This stage is quicker, more economical, and more controlled than the pre-training stage.

    Main Points Explained Clearly

    Conclusion

    General intelligence is cultivated using pre-training, while specialization in expert knowledge is achieved through

    Data

    It uses broad, unstructured, and diverse data for pre-training. Fine-tuning requires curated, labeled, or instruction-driven data.

    Cost and Effort

    The pre-training process involves very high costs and requires large AI labs. However, fine-tuning is relatively cheap and can be done by enterprises.

    Model Behavior

    After pre-training, it knows “a little about a lot.” Then, after fine-tuning, it knows “a lot about a little.”

    A Practical Analogy

    Think of a doctor.

    • “Pre-training” is medical school, wherein the doctor acquires education about anatomy, physiology, and general medicine.
    • Fine-tuning refers to specialization. It may include specialties such as cardiology or
    • Specialization is impossible without pre-training. Fine-tuning is necessary for the doctor to remain specialist.

    Why Fine-Tuning Is Significant for Real-World Systems

    Raw pre-trained models aren’t typically good enough in production contexts. There’s a benefit to fine-tuning a:

    • Decrease hallucinations in critical domains
    • Enhance consistency and reliability
    • synchronize results with legal stipulations
    • Adapt to local language, work flows, and terms

    It is even more critical within industries such as the medical sector, financial sectors, and government institutions that require accuracy and adherence.

    Fine-Tuning vs Prompt Engineering

    It should be noted that fine-tuning is not the same as prompt engineering.

    • Prompt engineering helps to steer the model’s conduct by providing more refined instructions, without modifying the model.
    • No, fine-tuning simply adjusts internal model parameters, making it behave in a predictable manner for all inputs.
    • Organizations begin their journey of machine learning tasks from prompt engineering to fine-tuning when greater control is needed.

    Whether a fine-tuning task can replace

    No. Fine-tuning is wholly reliant upon the knowledge derived during pre-trained models. There is no possibility of deriving general intelligence using fine-tuning with small data sets—it only molds and shapes what already exists or is already present.

    In Summary

    Pre-training represents the foundation of understanding in data and language that AI systems have, while fine-tuning allows them to apply this knowledge in task-, domain-, and expectation-specific ways. Both are essential for what constitutes the spine of the development of modern artificial intelligence.

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daniyasiddiquiEditor’s Choice
Asked: 26/12/2025In:

foundation models differ from task-sp ...

ai modelsartificial intelligencedeep learningfoundation modelsmachine learningmodel architecture
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    The Meaning of Ground From a higher perspective, the distinction between foundation models and task-specific AI models is based on scope and purpose. In other words, foundation models constitute general intelligence engines, while task-specific models have a singular purpose accomplishing a single tRead more

    The Meaning of Ground

    From a higher perspective, the distinction between foundation models and task-specific AI models is based on scope and purpose. In other words, foundation models constitute general intelligence engines, while task-specific models have a singular purpose accomplishing a single task.

    Foundation models might be envisioned as highly educated generalists, while task-specific models might be considered specialists trained to serve only one role in society.

    What Are Foundation Models?

    Foundation models are large-scale AI models. They require vast and diverse data sets. These data sets involve various domains like language, images, code, audio, and structure. Foundation models are not trained on a fixed task. They learn universal patterns and then convert them into task-specific models.

    Once trained, the same foundation model can be applied to the following tasks:

    • Text generation
    • Question Answering
    • Summar
    • Translation
    • Image understanding
    • Code assistance
    • Data analysis

    “These models are ‘ foundational’ because a variety of applications are built upon these models using a prompt, fine-tuning, or a light-weight adapter. ”

    What Are Task-Specific AI Models?

    The models are trained using a specific, narrow objective. Models are built, trained, and tested based on one specific, narrowly defined task.

    These include:

    • An email spam classifier
    • A face recognition system.
    • Medical Image Tumor Detector
    • A credit default prediction model
    • A speech-to-text engine for a given language

    These models are not meant for generalization for a domain other than their use case. For any domain other than their trained tasks, their performance abruptly deteriorates.

    Differences Explained in Simple Terms

    1. Scope of Intelligence

    Foundation models generalize the learned knowledge and can perform a large number of tasks without needing additional training. Task-specific models specialize in a single task or a single specific function and cannot be readily adapted or applied to other tasks.

    2. Training Methodology

    Foundation models are trained once on large datasets and are computationally intensive. Task-specific models are trained on smaller datasets but are specific to the task they are meant to serve.

    3. Reusability & Adapt

    An existing foundation model can be easily applied to different teams, departments, or industries. In general, a task-specific model will have to be recreated or retrained for each new task.

    4. Cost and Infrastructure

    Nonetheless, training a foundation model is costly but efficient in the use of models since they accomplish multiple tasks. Training task-specific models is rather inexpensive but turns costly if multiple models have to be developed.

    5. Performance Characteristics

    Task-specific models usually perform better than foundation models on a specific task. But for numerous tasks, foundation models provide “good enough” solutions that are much more desirable in practical systems.

    Actual Example

    Consider a hospital network.

    A foundation model can:

    1. Generate

    • Summarize patient files
    • Respond to questions from clinicians.
    • Create discharge summaries
    • Translation of medical records
    • Provide help regarding coding and billing questions

    Task-specific models could:

    • Pneumonia identification from chest X-rays alone
    • Both are important, but they are quite different.

    Why Foundation Models Are Gaining Popularity

    Organisations have begun to favor foundation models because they:

    • Cut the need for handling scores of different models
    • Accelerate adoption of AI solutions by other departments in
    • Allow fast experimentation with prompts over having to retrain
    • Support multimodal workflows (text + image + data combined)

    This has particular importance in business, healthcare, finance, and e-governance applications, which need to adapt to changing demands.

    Even when task-specific models are still useful

    Although foundation models have become increasingly popular, task-specific models continue to be very important for:

    • Approvals need to be deterministic
    • Very high accuracy is required for one task
    • Latency and compute are very constrained.
    • The job deals with sensitive or controlled data

    In principle many existing mature systems would employ foundation models for general intelligence and task-specific models for critical decision-making.

    In Summary

    Foundation models add the ingredient of width or generic capability with scalability and adaptability. Task-specific models add the ingredient of depth or focused capability with efficiency. Contemporary AI models and applications increasingly incorporate the best aspects of the first two models.

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daniyasiddiquiEditor’s Choice
Asked: 25/12/2025In:

expected to outperform in the next 6– ...

equitysectorsgrowthsectorsinvestingmarketforecastsectoroutlookstockmarket
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. Technology and AI-Driven Innovation The technology sector still leads all future growth narratives in most of the world. While there are concerns about valuations, those companies that are leading in artificial intelligence, cloud computing, data infrastructure, and cybersecurity should continueRead more

    1. Technology and AI-Driven Innovation

    The technology sector still leads all future growth narratives in most of the world. While there are concerns about valuations, those companies that are leading in artificial intelligence, cloud computing, data infrastructure, and cybersecurity should continue to expand their earnings and outperform their peers. AI investment has been one of the leading themes and should drive multi-year growth as AI goes from experimental budgets into core business strategy across industries.

    Within this theme:

    • AI software and services are in high demand: as enterprises embrace increasing amounts of AI to further automation, analytics, and customer engagement.
    • Cybersecurity: As every sphere has started to undergo a digital transformation, the need for advanced security, for sure, has been ripe; cybersecurity companies hence are very lucrative sectors.
    • Data infrastructure: Growth in data centers and cloud services underpins demand for networking, storage, and compute capabilities.

    Key Driver: Sustained corporate investment in digital transformation and cloud ecosystems.

    2. Financials: Banks, NBFCs, Insurance

    Financials tend to do well early to mid-cycle, and several factors suggest that this could continue:

    • The cyclical improvement in net interest margins with expanding credit demand and increased transactional activity is a boon to banks and financial institutions as countries’ economies grow.
    • Insurance companies may outperform due to rising penetration and demand for risk protection in both emerging and developed markets.

    It is banking and NBFCs, which several brokers and analysts in India hail as benefiting the most from credit growth, besides stabilizing valuations.

    Key driver: Financials earnings recovery and broader economic normalization.

    3. Automotive and Mobility

    Where supported by government policy or innovation, the automotive sector is seen to continue with strong growth momentum:

    • As such, projected volume increases coupled with supportive measures-meaning tax incentives-point to continued expansion in both passenger and commercial vehicle demand in India.
    • Global trends include electrification and mobility services, pulling investment and consumer adoption forward.

    Key driver: Policy support; resilient consumer spending.

    4. Health and Pharmaceuticals

    Health Care has been a structurally sound industry because of favorable demographics, innovation, and being a defensive industry:

    • Underpinning long-term demand are aging populations and higher healthcare utilization in many markets.
    • The integration of AI in diagnostics, treatment planning, and drug discovery further enhances growth opportunities.

    In countries like India, pharmaceuticals, hospitals, and CDMOs remained in focus for their strong fundamentals.

    Key driver: Secular demand for medical services and innovation.

    5. Consumer and Consumption-Led Sectors

    Consumer discretionary and staples sectors would likely gain from this, where income growth and strong consumption patterns are seen to exist. The list includes:

    • Consumer goods and retail segments capturing the rising middle-class demand.
    • Fast-moving consumer goods, FMCG, usually exhibit resilience even in any economical or uneven environment. In India, analysts especially point out that FMCG is the most favored sector by macro observers.

    Key driver: Shifting consumption patterns and resilience in the face of uncertainty.

    6. Industrials, Infrastructure, and Capital Goods

    Global and regional outlooks would also suggest that infrastructure spending and industrial demand may contribute meaningfully to earnings growth:

    • Infrastructure investment, defense contracts, and capital goods orders tend to rise sharply during periods of fiscal stimulus.
    • The utilities and energy infrastructure, including renewables capacity build-out, may offer stable performance with defensive qualities.

    Key driver: Infrastructure and industrial capacity investment by the government.

    7. Renewable Energy and Clean Tech

    The transition to clean energy systems continues to mature, supported by policy frameworks and declines in the cost of technologies such as solar and wind. Renewable energy companies, storage solutions, and related supply chains are well-positioned to thrive with increasingly global investment in cleantech.

    Key driver: Long-term climate commitments and technology cost parity.

    8. Precious Metals and Alternative Plays

    While they are not traditional sectors for equity, precious metals such as gold and silver often do exceptionally well during times of unease or at a time when there could be policy loosenings, such as rate cuts. Recent forecasts indicate that bullion markets will continue to see investor interest in 2026. Times of India.

    Key driver: Safe-haven demand due to macro volatility.

    Bringing It Together: What This Means for Investors

    • Diversification matters: No single sector has outperformed across all economic scenarios. Balancing exposure to growth themes such as technology and financials with defensive or cyclical plays like healthcare, consumer staples, and utilities helps to balance risk.
    • The macro context is critical:  Sectors that have policy tailwinds-for instance, infrastructure or renewable energy-tend to outperform when government spending and incentives are strong.
    • Valuations and earnings are the anchor: Long-term sector performance is driven by the underlying earnings growth, not short-term sentiment.

    Closing Thought

    No sector outperforms continuously without pauses. Over the next 6–12 months, key areas that could see upside, led by current market dynamics and structural trends, would be technology (in particular AI), financials, healthcare, consumer staples, and renewable energy. Cyclical sectors like industrials and automotive could also do well where the economy is stabilizing. Always evaluate risk and valuation against thematic strength before committing capital.

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daniyasiddiquiEditor’s Choice
Asked: 25/12/2025In:

a correction or a sustained rally

bullmarketinvestingmarketcorrectionmarketvolatilitystockmarketoutlookstockmarkettrends
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Why the Market Still Looks Strong One of the key factors that sustains the rise in the markets is the resilience in earnings. Large companies continue to report positive earnings trends in many markets, whichboosts market sentiment that businesses will succeed even in trying times. What markets geneRead more

    Why the Market Still Looks Strong

    One of the key factors that sustains the rise in the markets is the resilience in earnings. Large companies continue to report positive earnings trends in many markets, whichboosts market sentiment that businesses will succeed even in trying times. What markets generally need is a sharp decline in their earnings.

    An important push in this direction has come through increased liquidity. Even with a tight monetary trend in the past few years, a considerable amount of money has entered the stock market through mutual funds and institutional investments. There has also been a rise in public participation in the market through online platforms.

    Another is market sentiment. Markets can move not only on factual information, but also on market expectations. Market participants will typically look ahead to a bright future once they think that either inflation, or interest rates, or economic slowdown is behind them.

    Why a Correction Cannot Be Ruled Out

    The recent market behavior is

    On the other hand, warning signs are apparent too. In many industries, equity valuations are extended, which means that stock market values have grown faster than fundamentals. Eventually, when equity valuations run ahead of earnings power, good news is no longer enough to support further gains.

    Another area of worry is the level of market volatility. Sharp rallies followed by steep correction killings reveal nervous market participants, although it is a reality of markets, especially when driven more by market sentiment.

    There may also be some external risks. These may include global tensions in politics and geopolitics, unforeseen changes in policies, a slow-down in the global economy, and unexpected fluctuations in crude oil and currency markets. Such events can cause profit-booking in a short while due to increased uncertainty.

    What History Teaches Investors

    In the past, markets have seldom traced a linear pattern. Corrections are a normal and necessary part of a bull market. These corrections work to cool off speculation while providing a better buying entry point to the disciplined investor.

    Correction does not always mean that a rally has ended. There have been many instances in the past cycles where correction occurred multiple times before the market moved ahead.

    What Investors Need to Consider About this Transition Period

    Investors have to

    Instead of attempting to project a precise outcome, it would be far better off to prepare for both eventualities. This entails:

    Being cautious about using high leverage or being overly concentrated in one sector

    A more careful selection of fundamentally sound companies rather than trying to buy into the hottest stocks

    Diversifying by sectors and asset classes

    Remaining invested with the long-term perspective in mind instead of emotionally investing in the short-term trends

    Long-term investors find that correction periods offer buying opportunities, while for traders, effective risk management is the key strategy for success.

    The Balanced Reality

    The market is neither leaning towards a correction nor a strong rally—it is essentially testing both at the same time. Data-driven strength in the market is helping the upside, while high valuations are triggering correction concerns.

    In a nutshell, the market may march further up, but it may not do so without intervals, fluctuations, and times of correction in between. Those investors who understand these dynamics and move forward with patience rather than predictions are generally the ones who perform best during these times.

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daniyasiddiquiEditor’s Choice
Asked: 25/12/2025In:

learning replace traditional grading ...

assessmentmethodseducationreformlearningoutcomesmasterylearningstudentcenteredlearningtraditionalgrading
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Comprehension of Traditional Grading Systems Traditional grading patterns always involve either percentage grading, letter grading, and grade point average. All such grading patterns are designed for ranking students with regard to performance in exams, assignments, and attendance during a certain pRead more

    Comprehension of Traditional Grading Systems

    Traditional grading patterns always involve either percentage grading, letter grading, and grade point average. All such grading patterns are designed for ranking students with regard to performance in exams, assignments, and attendance during a certain period of time. Although grading is easy and common, it tends to emphasize performance in a particular time period and may fail to emphasize general comprehension.

    A number of students are normally faced with stress when grades are involved; in some instances, students may place grades above learning. A student may learn by heart in order to get percentages in an exam but forget as quickly as the exam is completed.

    In such learning environments where grades are involved, grades tend to evaluate one’s ability to pass in an exam rather than knowledge or skills acquired in an exam or a particular subject.

    What is Competency-Based Learning?

    Competency-based learning emphasizes what learners can actually accomplish rather than their time spent in a classroom setting or their ability in comparison with their peers. Rather than receiving a grade based on averages, learners must prove their proficiency in a certain skill before advancing to the next skill.

    Learning becomes flexible and adaptive. Students learn at their own pace, going back and mastering concepts until they become clear and understood. Such a method promotes understanding, conviction, and learning as opposed to hurriedness or competition.

    Why Competency-Based Learning Feels More Human

    Competency Based Learning acknowledges that students understand in different ways. While some gain an understanding quickly, some may require additional time to comprehend. It does not categorize slow learners as “weak” learners because it gives them an opportunity to succeed.

    It promotes a growth mindset. Failures or mistakes made during learning and practice are not treated as failures but as learning experiences. Students learn to work on improvement and development of skills and themselves.

    Effect of Learning Outcomes and Real-World Skills

    In reality, success is not determined by academic achievement in the classroom but by skills, intelligence in solving problems, and ability to adapt. Competencies are more suited to the work environment since performance is based on what one can deliver as compared to what one did in the classroom.

    Those who undergo the program develop greater skills with regard to critical thinking, teamwork, and application. This is because they are able to relate what they have learned to practical contexts.

    Issues Associated with Traditional Grading

    Despite its advantages, competency-based learning faces challenges. A major one is standardization. Grades serve as a swift and standardized means for comparing students for either college admission or employment purposes. To adopt a new means of assessment will have sweeping implications.

    Another challenge is that of implementation. Teachers will require training on how such competency will be assessed. Time and resources will also be required on their part for such an assessment of competency. A framework should be put in place by schools such that fairness and consistency

    A Balanced and Practical Approach

    Instead of using traditional methods of grading, many experts recommend a combination of the two. Both grades and competency measures can exist simultaneously, thus enabling both benchmarks and analysis to be achieved.

    Thus, this hybrid model maintains the ease of understanding that comes with the current grade system while providing valuable meaning in terms of skill mastery and performance assessments.

    A Thoughtful Way Forward

    Competency-based learning is a much more humane, flexible, and meaningful measure of learning. Competency-based learning will not necessarily replace traditional methods of grading on its own immediately, but it certainly will make one rethink what success means in an educational environment.

    “The best aim is not to get rid of the concept of grading, but to make sure that education focuses on understanding instead of memory, on growth instead of comparison, and on learning instead of labeling.” When education is centered on how the learning process functions, results will improve in all aspects of learning from an academic standpoint to a personal or professional capacity.

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