# ISC Class 12 Computer Science Syllabus 2024-25

CISCE has released the Latest Updated Syllabus of the New Academic Session 2024-25, for class 12. It is available under the ‘‘Regulations and Syllabuses’ page of ISC 2025 on www.cisce.org.

Class 12th Syllabus has been released by CISCE. It’s very important for both Teachers and Students to understand the changes and strictly follow the topics covered in each subject under each stream for Class 12th.

We have also updated Oswal Gurukul Books as per the Latest Paper Pattern prescribed by CISCE Board for each Subject Curriculum.

Students can directly access the ISC Computer Science Syllabus for Class 12 of the academic year 2024-25 by clicking on the link below.

PDF download links to the **latest** Class 12 Computer Science Syllabus for 2024-25 academic session

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## ISC Computer Science Class 12 Latest Syllabus 2024-25

There will be two papers in the subject:

**Paper I:** Theory: 3 hours -----70 marks

**Paper II:** Practical Work ------ 30 marks

**Paper I (Theory) – 70 Marks**

**SECTION – A**

**1. Boolean Algebra **

- Propositional logic, well formed formulae, truth values and interpretation of well formed formulae (wff), truth tables, satisfiable, unsatisfiable and valid formulae. Equivalence laws and their use in simplifying wffs.

Propositional variables; the common logical connectives (~ a (not)(negation), ∧ (and)(conjunction), ∨ (or)(disjunction), ⇒ (implication), ⇔ (biconditional); definition of well-formed formula (wff); `representation of simple word problems as wff (this can be used for motivation); the values true and false; interpretation of a wff; truth tables; satisfiable, unsatisfiable and valid formulae.

Equivalence laws: commutativity of ∧, ∨; associativity of ∧, ∨; distributivity; De Morgan’s laws; law of implication (p ⇒ q ≡ ~p ∨ q); law of biconditional ((p ⇔ q) ≡ (p ⇒ q) ∧ (q ⇒ p)); identity (p ≡ p); law of negation (~ (~p) ≡ p); law of excluded middle (p ∨~p ≡ true); law of contradiction (p∧~p ≡ false); tautology and contingency simplification rules for ∧, ∨. Converse, inverse and contra positive. Chain rule, Modus ponens.

- Binary valued quantities; basic postulates of Boolean algebra; operations AND, OR and NOT; truth tables.
- Basic theorems of Boolean algebra (e.g. duality, idempotence, commutativity, associativity, distributivity, operations with 0 and 1, complements, absorption, involution); De Morgan’s theorem and its applications; reducing Boolean expressions to sum of products and product of sums forms; Karnaugh maps (up to four variables).

Verify the laws of Boolean algebra using truth tables. Inputs, outputs for circuits like half and full adders, majority circuit etc., SOP and POS representation; Maxterms & Minterms, Canonical and Cardinal representation, reduction using Karnaugh maps and Boolean algebra.

**2. Computer Hardware **

(a) Elementary logic gates (NOT, AND, OR, NAND, NOR, XOR, XNOR) and their use in circuits.

(b) Applications of Boolean algebra and logic gates to half adders, full adders, encoders, decoders, multiplexers, NAND, NOR as universal gates

Show the correspondence between Boolean methods and the corresponding switching circuits or gates. Show that NAND and NOR gates are universal by converting some circuits to purely NAND or NOR gates

** SECTION B **

The programming element in the syllabus (Sections B and C) is aimed at algorithmic problem solving and not merely rote learning of Java syntax. The Java version used should be 5.0 or later. For programming, the students can use any text editor and the javac and java programs or any other development environment: for example, BlueJ, Eclipse, NetBeans etc. BlueJ is strongly recommended for its simplicity, ease of use and because it is very well suited for an ‘objects first’ approach.

**3. Implementation of algorithms to solve problems **

The students are required to do lab assignments in the computer lab concurrently with the lectures. Programming assignments should be done such that each major topic is covered in at least one assignment. Assignment problems should be designed so that they are sufficiently challenging. Students must do algorithm design, address correctness issues, implement and execute the algorithm in Java and debug where necessary.

**4. Programming in Java (Review of Class XI Sections B and C) **

Programming in Java (Review of Class XI Sections B and C)

While reviewing, ensure that new higher order problems are solved using these constructs.

**5. Objects **

- Objects as data (attributes) + behaviour (methods); object as an instance of a class. Constructors.
- Analysis of some real-world programming examples in terms of objects and classes.
- Basic input/output using Scanner and Printer classes from JDK; input/output exceptions. Tokens in an input stream, concept of whitespace, extracting tokens from an input stream (String Tokenizer class).

**6. Primitive values, Wrapper classes, Types and casting **

Primitive values and types: byte, int, short, long, float, double, boolean, char. Corresponding wrapper classes for each primitive type. Class as type of the object. Class as mechanism for user defined types. Changing types through user defined casting and automatic type coercion for some primitive types.

**7. Variables, Expressions**

Variables as names for values; named constants (final), expressions (arithmetic and logical) and their evaluation (operators, associativity, precedence). Assignment operation; difference between left hand side and right hand side of assignment.

**8. Statements, Scope**

Statements; conditional (if, if else, if else if, switch case, ternary operator), looping (for, while, do while, continue, break); grouping statements in blocks, scope and visibility of variables.

**9. Methods**

Methods (as abstractions for complex user defined operations on objects), formal arguments and actual arguments in methods; different behaviour of primitive and object arguments. Static method and variables. The this Operator. Examples of algorithmic problem solving using methods (number problems, finding roots of algebraic equations etc.).

**10. Arrays, Strings **

Structured data types – arrays (single and multidimensional), address calculations, strings. Example algorithms that use structured data types (e.g. searching, finding maximum/minimum, sorting techniques, solving systems of linear equations, substring, concatenation, length, access to char in string, etc.).

Storing many data elements of the same type requires structured data types – like arrays. Access in arrays is constant time and does not depend on the number of elements. Address calculation (row major and column major), Sorting techniques (bubble, selection, insertion). Structured data types can be defined by classes – String. Introduce the Java library String class and the basic operations on strings (accessing individual characters, various substring operations, concatenation, replacement, index of operations). The class StringBuffer should be introduced for those applications that involve heavy manipulation of strings.

**11. Recursion **

Concept of recursion, simple recursive methods (e.g. factorial, GCD, binary search, conversion of representations of numbers between different bases).

Many problems can be solved very elegantly by observing that the solution can be composed of solutions to ‘smaller’ versions of the same problem with the base version having a known simple solution. Recursion can be initially motivated by using recursive equations to define certain methods. These definitions are fairly obvious and are easy to understand. The definitions can be directly converted to a program. Emphasize that any recursion must have a base case. Otherwise, the computation can go into an infinite loop.

The tower of Hanoi is a very good example of how recursion gives a very simple and elegant solution where as non-recursive solutions are quite complex.

**SECTION C**

Inheritance, Interface, Polymorphism, Data structures, Computational complexity

**12. Inheritance, Interfaces and Polymorphism **

- Inheritance; super and derived classes; member access in derived classes; redefinition of variables and methods in subclasses; abstract classes; class Object; protected visibility. Subclass polymorphism and dynamic binding.

Emphasize inheritance as a mechanism to reuse a class by extending it. Inheritance should not normally be used just to reuse some methods defined in a class but only when there is a genuine specialization (or subclass) relationship between objects of the super class and that of the derived class. - Interfaces in Java; implementing interfaces through a class; interfaces for user defined implementation of behaviour.

Motivation for interface: often when creating reusable classes some parts of the exact implementation can only be provided by the final end user. For example, in a class that sorts records of different types the exact comparison operation can only be provided by the end user. Since only he/she knows which field(s) will be used for doing the comparison and whether sorting should be in ascending or descending order be given by the user of the class. Emphasize the difference between the Java language construct interface and the word interface often used to describe the set of method prototypes of a class.

**13. Data structures**

- Basic data structures (stack, queue, circular queue, dequeue); implementation directly through classes; definition through an interface and multiple implementations by implementing the interface. Conversion of Infix to Prefix and Postfix notations.

Basic algorithms and programs using the above data structures.

Data structures should be defined as abstract data types with a well-defined interface (it isnstructive to define them using the Java interface construct). - Single SECTION C linked list (Algorithm and programming), binary trees, tree traversals (Conceptual).

The following should be covered for each data structure:

Linked List (single): insertion, deletion, reversal, extracting an element or a sublist, checking emptiness.

Linked List (single): insertion, deletion, reversal, extracting an element or a sublist, checking emptiness.

**14. Complexity and Big O notation **

Concrete computational complexity; concept of input size; estimating complexity in terms of methods; importance of dominant term; constants, best, average and worst case.

Big O notation for computational complexity; analysis of complexity of example algorithms using the big O notation (e.g. Various searching and sorting algorithms, algorithm for solution of linear equations etc.).

**Paper II : Practical – 30 MARKS**

This paper of three hours’ duration will be evaluated by the Visiting Examiner appointed locally and approved by CISCE.

The paper shall consist of three programming problems from which a candidate has to attempt any one. The practical consists of the two parts:

1. Planning Session

2. Examination Session

The total time to be spent on the Planning session and the Examination session is three hours. A maximum of 90 minutes is permitted for the Planning session and 90 minutes for the Examination session.

Candidates are to be permitted to proceed to the Examination Session only after the 90 minutes of the Planning Session are over.

**Planning Session**

The candidates will be required to prepare an algorithm and a hand written Java program to solve the problem.

**Examination Session**

The program handed in at the end of the Planning session shall be returned to the candidates. The candidates will be required to key-in and execute the Java program on seen and unseen inputs individually on the Computer and show execution to the Visiting Examiner. A printout of the program listing including output results should be attached to the answer script containing the algorithm and handwritten program. This should be returned to the examiner. The program should be sufficiently documented so that the algorithm, representation and development process is clear from reading the program. Large differences between the planned program and the printout will result in loss of marks.

Teachers should maintain a record of all the assignments done as part of the practical work through the year and give it due credit at the time of cumulative evaluation at the end of the year. Students are expected to do a minimum of twenty-five assignments for the year.

**EVALUATION:**

Marks (out of a total of 30) should be distributed as given below:

**Continuous Evaluation**

Candidates will be required to submit a work file containing the practical work related to programming assignments done during the year.

Programming assignments throughout the done year (Internal Evaluation) | 10 marks |

Programming assignments done throughout the year (Visiting Examiner) | 5 marks |

**Terminal Evaluation**

Solution to programming problem on the computer | 15 marks |

Marks should be given for choice of algorithm and implementation strategy, documentation, correct output on known inputs mentioned in the question paper, correct output for unknown inputs available only to the examiner.