Coaching Institutes for Class 11 - 12 in Ranchi

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BHARAT CLASSES is the best tuition Centre for XI/XII students who are preparing for IIT-JEE / MEDICAL. Contact Details Mobile : 9835376044 / 9006365889 / 9199847964 Durga Mandir Lane , Chunna Bhatta , Kokar ,Ranchi-834001,Jharkhand Website : www.bharatsir.com

Coaching Institutes for Class 11 - 12 in Ranchi

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Dynamic Programming : Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems, solving each subproblem once, and storing the solution to each subproblem so that it can be reused in the future. Some characteristics of dynamic programming include: Optimal substructure: Dynamic programming problems typically have an optimal substructure, meaning that the optimal solution to the problem can be obtained by solving the subproblems optimally and combining their solutions. Overlapping subproblems: Dynamic programming problems often involve overlapping subproblems, meaning that the same subproblems are solved multiple times. To avoid solving the same subproblem multiple times, dynamic programming algorithms store the solutions to the subproblems in a table or array, so that they can be reused later. Bottom-up approach: Dynamic programming algorithms usually solve problems using a bottom-up approach, meaning that they start by solving the smallest subproblems and work their way up to the larger ones. Efficiency: Dynamic programming algorithms can be very efficient, especially when the subproblems overlap significantly. By storing the solutions to the subproblems and reusing them, dynamic programming algorithms can avoid redundant computations and achieve good time and space complexity. Applicability: Dynamic programming is applicable to a wide range of problems, including optimization problems, decision problems, and problems that involve sequential decisions. It is often used to solve problems in computer science, operations research, and economics. Algorithm Design Techniques Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.

Algorithm Design Technique

Algorithm Design Technique

Algorithm Design Technique

Problem Characteristics in Artificial Intelligence, Unit -2 Problem Solving and Searching Techniques o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics. Is the problem decomposable into small sub-problems which are easy to solve? Can solution steps be ignored or undone? Is the universe of the problem is predictable? Is a good solution to the problem is absolute or relative? Is the solution to the problem a state or a path? What is the role of knowledge in solving a problem using artificial intelligence? Does the task of solving a problem require human interaction? 1. Is the problem decomposable into small sub-problems which are easy to solve? Can the problem be broken down into smaller problems to be solved independently? See also Water Jug Problem in Artificial Intelligence The decomposable problem can be solved easily. Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result. Is the problem decomposable 2. Can solution steps be ignored or undone? In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps. Such problems are called Ignorable problems. In the 8-Puzzle, Moves can be undone and backtracked. Such problems are called Recoverable problems. In Playing Chess, moves can be retracted. Such problems are called Irrecoverable problems. Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning. 3. Is the universe of the problem is predictable? In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns. Uncertain outcome! For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution. For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided. 4. Is a good solution to the problem is absolute or relative? The Travelling Salesman Problem, we have to try all paths to find the shortest one. See also Generate and Test Heuristic Search - Artificial Intelligence Any path problem can be solved using heuristics that suggest good paths to explore. For best-path problems, a much more exhaustive search will be performed. 5. Is the solution to the problem a state or a path The Water Jug Problem, the path that leads to the goal must be reported.

Production System in AI

Production System in AI

Production System in AI

Production System in Artificial Intelligence (AI) A production system in AI helps create AI-based computer programs. With the help of it, the automation of various types of machines has become an easy task. The types of machines can be a computer, mobile applications, manufacturing tools, or more. The set of rules in a production system in Artificial Intelligence defines the behavior of the machine. It helps the machine respond to the surroundings. A production system in AI is a type of cognitive architecture that defines specific actions as per certain rules. The rules represent the declarative knowledge of a machine to respond according to different conditions. Today, many expert systems and automation methodologies rely on the rules of production systems. Global Database A global database consists of the architecture used as a central data structure. A database contains all the necessary data and information required for the successful completion of a task. It can be divided into two parts as permanent and temporary. The permanent part of the database consists of fixed actions, whereas the temporary part alters according to circumstances. Learn more about Artificial Neural networks in this insightful Artificial Intelligence Training now! Production Rules Production rules in AI are the set of rules that operates on the data fetched from the global database. Also, these production rules are bound with precondition and postcondition that gets checked by the database. If a condition is passed through a production rule and gets satisfied by the global database, then the rule is successfully applied. The rules are of the form A®B, where the right-hand side represents an outcome corresponding to the problem state represented by the left-hand side. Control System The control system checks the applicability of a rule. It helps decide which rule should be applied and terminates the process when the system gives the correct output. It also resolves the conflict of multiple conditions arriving at the same time. The strategy of the control system specifies the sequence of rules that compares the condition from the global database to reach the correct result. Simplicity The production rule in AI is in the form of an ‘IF-THEN’ statement. Every rule in the production system has a unique structure. It helps represent knowledge and reasoning in the simplest way possible to solve real-world problems. Also, it helps improve the readability and understanding of the production rules.

Production System in AI

Production System in AI

Production System in AI

Problem Characteristics in Artificial Intelligence, Unit -2 Problem Solving and Searching Techniques o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics. Is the problem decomposable into small sub-problems which are easy to solve? Can solution steps be ignored or undone? Is the universe of the problem is predictable? Is a good solution to the problem is absolute or relative? Is the solution to the problem a state or a path? What is the role of knowledge in solving a problem using artificial intelligence? Does the task of solving a problem require human interaction? 1. Is the problem decomposable into small sub-problems which are easy to solve? Can the problem be broken down into smaller problems to be solved independently? See also Water Jug Problem in Artificial Intelligence The decomposable problem can be solved easily. Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result. Is the problem decomposable 2. Can solution steps be ignored or undone? In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps. Such problems are called Ignorable problems. In the 8-Puzzle, Moves can be undone and backtracked. Such problems are called Recoverable problems. In Playing Chess, moves can be retracted. Such problems are called Irrecoverable problems. Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning. 3. Is the universe of the problem is predictable? In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns. Uncertain outcome! For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution. For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided. 4. Is a good solution to the problem is absolute or relative? The Travelling Salesman Problem, we have to try all paths to find the shortest one. See also Generate and Test Heuristic Search - Artificial Intelligence Any path problem can be solved using heuristics that suggest good paths to explore. For best-path problems, a much more exhaustive search will be performed. 5. Is the solution to the problem a state or a path The Water Jug Problem, the path that leads to the goal must be reported.

Problem Characteristics in Artificial Intelligence

Problem Characteristics in Artificial Intelligence

Problem Characteristics in Artificial Intelligence

Artificial Intelligence is a “way of making a computer, a computer-controlled robot, or software think intelligently, in the similar manner the intelligent humans think”. Since artificial intelligence (AI) is mainly related to the search process, it is important to have some methodology to choose the best possible solution. To choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics. Is the problem decomposable into small sub-problems which are easy to solve? Can solution steps be ignored or undone? Is the universe of the problem is predictable? Is a good solution to the problem is absolute or relative? Is the solution to the problem a state or a path? What is the role of knowledge in solving a problem using artificial intelligence? Does the task of solving a problem require human interaction? 1. Is the problem decomposable into small sub-problems which are easy to solve? Can the problem be broken down into smaller problems to be solved independently? See also Water Jug Problem in Artificial Intelligence The decomposable problem can be solved easily. Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result. Is the problem decomposable 2. Can solution steps be ignored or undone? In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps. Such problems are called Ignorable problems. In the 8-Puzzle, Moves can be undone and backtracked. Such problems are called Recoverable problems. In Playing Chess, moves can be retracted. Such problems are called Irrecoverable problems. Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning. 3. Is the universe of the problem is predictable? In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns. Uncertain outcome! For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution. For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided. 4. Is a good solution to the problem is absolute or relative? The Travelling Salesman Problem, we have to try all paths to find the shortest one. See also Generate and Test Heuristic Search - Artificial Intelligence Any path problem can be solved using heuristics that suggest good paths to explore. For best-path problems, a much more exhaustive search will be performed. 5. Is the solution to the problem a state or a path The Water Jug Problem, the path that leads to the goal must be reported.

Problem Characteristics in Artificial Intelligence

Problem Characteristics in Artificial Intelligence

Problem Characteristics in Artificial Intelligence

ARTIFICIAL INTELLIGENCE : Introduction , Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment. 2. Problem Solving and Searching Techniques Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A* algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing, Min-Max and Alpha-Beta pruning algorithms. 3. Knowledge Representation Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets, Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs. Programming in Logic (PROLOG) 4. Dealing with Uncertainty and Inconsistencies Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic Inference, Possible World Representations. 5. Understanding Natural Languages Parsing Techniques, Context-Free and Transformational Grammars, Recursive and Augmented Transition Nets.

Introduction to Artificial Intteligence

Introduction to Artificial Intteligence

Introduction to Artificial Intteligence

ARTIFICIAL INTELLIGENCE : Introduction , Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment. 2. Problem Solving and Searching Techniques Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A* algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing, Min-Max and Alpha-Beta pruning algorithms. 3. Knowledge Representation Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets, Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs. Programming in Logic (PROLOG) 4. Dealing with Uncertainty and Inconsistencies Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic Inference, Possible World Representations. 5. Understanding Natural Languages Parsing Techniques, Context-Free and Transformational Grammars, Recursive and Augmented Transition Nets.

Introduction to Artificial Intteligence

Introduction to Artificial Intteligence

Introduction to Artificial Intteligence

DESIGN AND ANALYSIS OF ALGORITHMS 1. Introduction Basic Design and Analysis techniques of Algorithms, Correctness of Algorithm. 2. Algorithm Design Techniques Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms. 3. Sorting and Searching Techniques Elementary sorting techniques–Bubble Sort, Insertion Sort, Merge Sort, Advanced Sorting techniques - Heap Sort, Quick Sort, Sorting in Linear Time - Bucket Sort, Radix Sort and Count Sort, Searching Techniques, Medians & Order Statistics, complexity analysis; 4. Lower Bounding Techniques Decision Trees 5. Balanced Trees Red-Black Trees 6. Advanced Analysis Technique Amortized analysis 7. Graphs Graph Algorithms–Breadth First Search, Depth First Search and its Applications, Minimum Spanning Trees.

DESIGN AND ANALYSIS OF ALGORITHMS by Bharat Sir

DESIGN AND ANALYSIS OF ALGORITHMS by Bharat Sir

DESIGN AND ANALYSIS OF ALGORITHMS by Bharat Sir

DESIGN AND ANALYSIS OF ALGORITHMS 1. Introduction Basic Design and Analysis techniques of Algorithms, Correctness of Algorithm. 2. Algorithm Design Techniques Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms. 3. Sorting and Searching Techniques Elementary sorting techniques–Bubble Sort, Insertion Sort, Merge Sort, Advanced Sorting techniques - Heap Sort, Quick Sort, Sorting in Linear Time - Bucket Sort, Radix Sort and Count Sort, Searching Techniques, Medians & Order Statistics, complexity analysis; 4. Lower Bounding Techniques Decision Trees 5. Balanced Trees Red-Black Trees 6. Advanced Analysis Technique Amortized analysis 7. Graphs Graph Algorithms–Breadth First Search, Depth First Search and its Applications, Minimum Spanning Trees.

DESIGN AND ANALYSIS OF ALGORITHMS

DESIGN AND ANALYSIS OF ALGORITHMS

DESIGN AND ANALYSIS OF ALGORITHMS

ARTIFICIAL INTELLIGENCE 1. Introduction Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment. 2. Problem Solving and Searching Techniques Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A* algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing, Min-Max and Alpha-Beta pruning algorithms. 3. Knowledge Representation Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets, Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs. Programming in Logic (PROLOG) 4. Dealing with Uncertainty and Inconsistencies Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic Inference, Possible World Representations. 5. Understanding Natural Languages Parsing Techniques, Context-Free and Transformational Grammars, Recursive and Augmented Transition Nets.

ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE 1. Introduction Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment. 2. Problem Solving and Searching Techniques Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A* algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing, Min-Max and Alpha-Beta pruning algorithms. 3. Knowledge Representation Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets, Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs. Programming in Logic (PROLOG) 4. Dealing with Uncertainty and Inconsistencies Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic Inference, Possible World Representations. 5. Understanding Natural Languages Parsing Techniques, Context-Free and Transformational Grammars, Recursive and Augmented Transition Nets.

ARTIFICIAL INTELLIGENCE SYLLABUS

ARTIFICIAL INTELLIGENCE SYLLABUS

ARTIFICIAL INTELLIGENCE SYLLABUS

Cyber Security

Cyber Security

रांची, 24 जुलाई : विश्व संवाद केंद्र झारखंड के तत्वाधान में डिजिटल कंटेंट क्रिएशन एंड यूटिलाइजेशन फॉर स्टूडेंट्स एंड इनफार्मेशन प्रोफेशनल विषय पर एक दिवसीय प्रशिक्षण कार्यक्रम का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, कोकर के सभागार में आयोजित किया गया। कार्यक्रम का उद्घाटन इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडे ने दीप प्रज्वलित कर किया । इंफॉर्मेशन साइंटिस्ट भारत भूषण ने छात्रों को डिजिटल कंटेंट क्रिएट करने और उसका उपयोग अपने और देश हित में कैसे कर सकते हैं इस विषय पर छात्रों को प्रशिक्षित किया। कार्यक्रम के समापन में मुख्य अतिथि के तौर पर राम लखन सिंह यादव कॉलेज के प्रिंसिपल डॉ. जे.पी. सिंह ने छात्रों को कहा कि आज का दौर टेक्नोलॉजी और इंफॉर्मेशन का है। आज वही सबसे ज्यादा विकसित राष्ट्र कहलाएगा जिसके पास जितना ज्यादा से ज्यादा विकसित टेक्नोलॉजी होगा। कार्यक्रम के दौरान बेहतर प्रदर्शन करने वाले छात्रों को डॉ. जे.पी. सिंह ने मेडल देकर सम्मानित किया। कार्यक्रम को सफल बनाने में सुप्रिया भारती, सावित्री कुमारी, हरेंद्र कुमार, निशांत दीप दास, छाया महतो, अंकित रंजन एवं किशोरी शाह का मुख्य योगदान रहा।

Digital Content Creation By Bharat Sir Kokar

Digital Content Creation By Bharat Sir Kokar

Digital Content Creation By Bharat Sir Kokar

रांची, 24 जुलाई : विश्व संवाद केंद्र झारखंड के तत्वाधान में डिजिटल कंटेंट क्रिएशन एंड यूटिलाइजेशन फॉर स्टूडेंट्स एंड इनफार्मेशन प्रोफेशनल विषय पर एक दिवसीय प्रशिक्षण कार्यक्रम का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, कोकर के सभागार में आयोजित किया गया। कार्यक्रम का उद्घाटन इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडे ने दीप प्रज्वलित कर किया । इंफॉर्मेशन साइंटिस्ट भारत भूषण ने छात्रों को डिजिटल कंटेंट क्रिएट करने और उसका उपयोग अपने और देश हित में कैसे कर सकते हैं इस विषय पर छात्रों को प्रशिक्षित किया। कार्यक्रम के समापन में मुख्य अतिथि के तौर पर राम लखन सिंह यादव कॉलेज के प्रिंसिपल डॉ. जे.पी. सिंह ने छात्रों को कहा कि आज का दौर टेक्नोलॉजी और इंफॉर्मेशन का है। आज वही सबसे ज्यादा विकसित राष्ट्र कहलाएगा जिसके पास जितना ज्यादा से ज्यादा विकसित टेक्नोलॉजी होगा। कार्यक्रम के दौरान बेहतर प्रदर्शन करने वाले छात्रों को डॉ. जे.पी. सिंह ने मेडल देकर सम्मानित किया। कार्यक्रम को सफल बनाने में सुप्रिया भारती, सावित्री कुमारी, हरेंद्र कुमार, निशांत दीप दास, छाया महतो, अंकित रंजन एवं किशोरी शाह का मुख्य योगदान रहा।

Digital Content Creation by Bharat Sir Kokar

Digital Content Creation by Bharat Sir Kokar

Digital Content Creation by Bharat Sir Kokar

10 जुलाई , 2022 : लॉर्ड गौतम बुद्धा ट्रस्ट एवं इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन, झारखंड के संयुक्त तत्वाधान में “ द इनफार्मेशन टेक्नोलॉजी एक्ट, 2000 ” पर सेमिनार का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, चूना भट्ठा, कोकर के सभागार में इंफॉर्मेशन साइंटिस्ट भारत भूषण के द्वारा दीप प्रज्वलित कर किया गया। उन्होंने कहा कि आज के डिजिटल युग में गलती से भी गलती ना हो इसके लिए जरूरी है कि आम व्यक्ति भी सूचना तकनीक अधिनियम 2000 के बारे में जाने। इस दौर में हर के हाथ में मोबाइल है डिजिटल युग में गलती से भी गलती ना करें : इंफॉर्मेशन साइंटिस्ट भारतऔर हर मोबाइल इंटरनेट से कनेक्टेड है। टचस्क्रीन मोबाइल होने के कारण कभी-कभी फोटो या वीडियो कहीं भी गलती से शेयर हो जाता है जिसका कितना भयानक परिणाम हो सकता है आज कल देखने को मिल रहा है।

NEET PHYSICS RANCHI

NEET PHYSICS RANCHI

NEET PHYSICS RANCHI

10 जुलाई , 2022 : लॉर्ड गौतम बुद्धा ट्रस्ट एवं इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन, झारखंड के संयुक्त तत्वाधान में “ द इनफार्मेशन टेक्नोलॉजी एक्ट, 2000 ” पर सेमिनार का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, चूना भट्ठा, कोकर के सभागार में इंफॉर्मेशन साइंटिस्ट भारत भूषण के द्वारा दीप प्रज्वलित कर किया गया। उन्होंने कहा कि आज के डिजिटल युग में गलती से भी गलती ना हो इसके लिए जरूरी है कि आम व्यक्ति भी सूचना तकनीक अधिनियम 2000 के बारे में जाने। इस दौर में हर के हाथ में मोबाइल है डिजिटल युग में गलती से भी गलती ना करें : इंफॉर्मेशन साइंटिस्ट भारतऔर हर मोबाइल इंटरनेट से कनेक्टेड है। टचस्क्रीन मोबाइल होने के कारण कभी-कभी फोटो या वीडियो कहीं भी गलती से शेयर हो जाता है जिसका कितना भयानक परिणाम हो सकता है आज कल देखने को मिल रहा है।

Bharat Sir Kokar

Bharat Sir Kokar

Bharat Sir Kokar

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Buddha Science and Technical Institute

Buddha Science and Technical Institute

Buddha Science and Technical Institute

10 जुलाई , 2022 : लॉर्ड गौतम बुद्धा ट्रस्ट एवं इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन, झारखंड के संयुक्त तत्वाधान में “ द इनफार्मेशन टेक्नोलॉजी एक्ट, 2000 ” पर सेमिनार का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, चूना भट्ठा, कोकर के सभागार में इंफॉर्मेशन साइंटिस्ट भारत भूषण के द्वारा दीप प्रज्वलित कर किया गया।

The Information Technology ACT, 2000

The Information Technology ACT, 2000

The Information Technology ACT, 2000

10 जुलाई , 2022 : लॉर्ड गौतम बुद्धा ट्रस्ट एवं इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन, झारखंड के संयुक्त तत्वाधान में “ द इनफार्मेशन टेक्नोलॉजी एक्ट, 2000 ” पर सेमिनार का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, चूना भट्ठा, कोकर के सभागार में इंफॉर्मेशन साइंटिस्ट भारत भूषण के द्वारा दीप प्रज्वलित कर किया गया।

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In BC’s nearly-decade-old “new” curriculum, the curricular competencies describe the processes that students are expected to develop in areas of learning such as mathematics. They reflect the “Do” in the “Know-Do-Understand” model. Under the “Communicating” header falls the curricular competency “Explain and justify mathematical ideas and decisions.” Note that it contains two processes: “Explain mathematical ideas” and “Justify mathematical decisions.” I have broken it down into its separate parts in order to understand--or reveal--its meaning. The first part is commonplace in classrooms. By now, BC math teachers—and students—understand that “Explain mathematical ideas” means more than “Show your work.” Teachers consistently ask “What did you do?” and “How do you know?” This process is about retelling, not just of steps but of thinking. The second part happens less frequently. Think back to the last time that you observed a student make—a necessary precursor to justify—a mathematical decision. “Justify” is about defending. Like “explain,” it involves reasoning; unlike “explain,” it also involves opinion and debate. In order to reinterpret the curricular competency “Explain and justify mathematical ideas and decisions,” I will continue to take apart its constituent part “Justify mathematical decisions” and carefully examine the term “mathematical decisions.” What, exactly, is a “mathematical decision”? Below, I will categorize answers to this question. These categories, and the provided examples, may help to suggest new opportunities for students to justify.

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