Summary

• Description:

  The MS Word document comprises Grade 10 CBC Core Mathematics Term 3 2026 Scheme of Work designed for senior school mathematics educators implementing the curriculum under the Kenya Institute of Curriculum Development (KICD) 2025 Competency-Based Curriculum framework. This instructional plan outlines a lesson-by-lesson sequence, serving as an indispensable resource for lesson planning, mathematical problem-solving drills, geometrical plotting, statistical project organization, and learner assessment during the third term.

  The scheme spans two major mathematical strands: Strand 3.0 (Measurements and Geometry) and Strand 4.0 (Statistics and Probability). In the opening weeks, learners investigate Linear Motion under Sub-Strand 3.1, defining scalar vs. vector quantities (distance, displacement, speed, velocity, acceleration), performing kinematic calculations, plotting and interpreting displacement-time graphs (velocity as gradient) and velocity-time graphs (acceleration as gradient, distance as area under curve), calculating relative speed for vehicles moving in the same or opposite directions, and applying relative speed to road safety situations. In the second half, the scheme covers Statistics I under Sub-Strand 4.2 (primary/secondary data, ungrouped/grouped frequency distribution tables, class limits/boundaries/midpoints/width, calculating mean (x-bar = sum(fx)/sum(f)), median, and modal class, constructing histograms with equal and unequal class widths using frequency density, and drawing frequency polygons) and Probability 1 under Sub-Strand 4.3 (experimental probability, probability scale 0-1, sample space grids, Mutually Exclusive events Addition Law P(A or B) = P(A) + P(B), Independent events Multiplication Law P(A and B) = P(A) x P(B), constructing probability tree diagrams for compound events, educational probability games, and road safety risk assessments).

  Key Scheme Details

  • Education Level: Grade 10 (Senior School)

  • Subject: Core Mathematics

  • Term: Term 3

  • Year: 2026

  • Curriculum: KICD Competency-Based Curriculum (CBC) 2025 Framework

  • Number of Weeks Covered: 9 Calendar Weeks

  • Number of Teaching Weeks: 8 Weeks

  • Number of Lessons per Week: 5 Lessons

  • Total Number of Lessons: 40 Lessons

  • Half-Term/Mid-Term Break: Week 7 (7 October 2026 - 11 October 2026)

  • Examination/Assessment Period: Week 9 (Paper 1 and Paper 2 Exams)

  • Strands Covered: Measurements and Geometry, Statistics and Probability, Core Mathematics Synthesis

  • Sub-Strands Covered: Linear Motion, Statistics I, Probability 1, Term Reviews, Integrated Project Evaluation and Assessments

  • Assessment Methods: Oral questions, written tests, practical observation, graph plotting evaluation, problem-solving tasks, table construction rubrics, histogram inspection, calculation checks, tree diagram checks, game participation, project portfolio scoring, written examination papers

  • Learning Resources: Grade 10 Core Mathematics Curriculum Design, graph paper, rulers, geometrical sets, stopwatches, tape measures, calculators, data collection templates, frequency tables, dice, coins, spinners, probability tree templates, road safety charts, past exam papers, marking schemes

  Weekly Coverage

  Week 1: Linear Motion (Kinematic Concepts and Graphs)
  Defining distance, displacement, speed, velocity, acceleration, calculating speed and average velocity, plotting displacement-time graphs, calculating gradient for velocity, and plotting velocity-time graphs.

  Week 2: Linear Motion (Graphs and Relative Speed)
  Calculating acceleration and distance (area under curve) from velocity-time graphs, relative speed for bodies moving in the same direction, relative speed for bodies moving in opposite directions (meeting/overtaking times), and road safety applications.

  Week 3: Statistics I (Data Organization and Central Tendency)
  Primary vs. secondary data sources, constructing frequency tables for ungrouped data, grouping raw data into class intervals (limits, boundaries, midpoints, width), identifying mode/modal class, and calculating median for grouped data.

  Week 4: Statistics I (Mean Calculations and Statistical Graphs)
  Calculating mean for grouped data (x-bar = sum(fx)/sum(f)), constructing histograms with equal class widths, calculating frequency density (Frequency/Class Width) for unequal class widths, plotting frequency polygons, and graphical data interpretation.

  Week 5: Statistical Mini-Project and Probability 1 Basics
  Presenting statistical mini-projects, experimental probability experiments (coins, dice), probability scale (0-1), sample spaces for single/combined events, and Mutually Exclusive events Addition Law (P(A or B) = P(A) + P(B)).

  Week 6: Probability 1 (Independent Events and Tree Diagrams)
  Independent events Multiplication Law (P(A and B) = P(A) x P(B)), combined addition and multiplication law problem solving, constructing probability tree diagrams, calculating compound probabilities along branches, and playing probability games.

  Week 7: Mid-Term Break
  Scheduled mid-term break from 7 October 2026 to 11 October 2026.

  Week 8: Probability Safety Applications and Comprehensive Sub-Strand Reviews
  Probability applications in road safety risk assessment, systematic review of Linear Motion formulas, review of Statistics I grouped data calculations, review of Probability 1 tree diagrams, and integrated term diagnostic tests.

  Week 9: End-of-Term Examinations, Project Evaluation and Closing
  Sitting for Written Examination Paper 1, sitting for Written Examination Paper 2, review of marking schemes, statistical/probability project portfolio scoring, resource inventorying, and term closing procedures.

  Strands and Sub-Strands Covered

  Strand 3.0: Measurements and Geometry

  Sub-Strand 3.1: Linear Motion

  • Scalar vs. vector quantities: distance vs. displacement, speed vs. velocity, and acceleration.

  • Kinematic formulas and speed/velocity/acceleration calculations.

  • Displacement-time graphs: plotting, scale selection, axis labeling, and velocity gradient calculation.

  • Velocity-time graphs: uniform acceleration, constant velocity, deceleration, gradient, and area calculations.

  • Relative speed principles: same direction (Vr = V1 - V2) vs. opposite direction (Vr = V1 + V2).

  • Time and distance of meeting/overtaking calculations and road safety overtaking hazards.

  Strand 4.0: Statistics and Probability

  Sub-Strand 4.2: Statistics I

  • Primary and secondary data collection methods (observation, questionnaires, interviews).

  • Ungrouped and grouped frequency distribution tables: class limits, boundaries, midpoints (x), width.

  • Measures of central tendency: mode, modal class, median estimation, and mean calculation (x-bar = sum(fx)/sum(f)).

  • Histograms: equal class widths vs. unequal class widths using frequency density (FD = Frequency/Class Width).

  • Frequency polygons: plotting midpoints against frequencies and boundary extensions.

  • Statistical inferences, graphical data interpretation, and school community mini-projects.

  Sub-Strand 4.3: Probability 1

  • Experimental vs. theoretical probability (Probability = Favorable Outcomes/Total Trials).

  • Probability range scale: 0 (impossible) to 1 (certain).

  • Sample space generation for single and compound events using lists and grids.

  • Mutually Exclusive events and the Addition Law: P(A or B) = P(A) + P(B).

  • Independent events and the Multiplication Law: P(A and B) = P(A) x P(B).

  • Probability tree diagrams: branch labeling, outcome path multiplication, and path addition.

  • Probability-based educational games, risk assessment, and road safety applications.

  Key Topics and Learning Areas

  Kinematics, Graphical Analysis, and Relative Motion

  • Distinguishing path length from directional displacement vectors.

  • Graphical calculus foundations: determining instantaneous/average velocity from graph slopes and total displacement from bounded graphical areas.

  • Vector sum and difference calculations in relative motion scenarios.

  Grouped Data Analysis, Central Tendencies, and Graphing

  • Transforming continuous and discrete raw data into structured class intervals.

  • Calculating weighted mean averages (sum(fx)/sum(f)) and interpolating median positions.

  • Constructing frequency density histograms where rectangular areas represent absolute frequencies.

  Probability Theory, Compound Tree Diagrams, and Game Analysis

  • Rigorous application of set addition and multiplication rules to compound probability problems.

  • Multi-stage tree branch probability construction (P = 1 at nodes) and path probability evaluation.

  • Mathematical expectation, game fairness evaluation, and risk modeling in real-life safety contexts.

  Learning Outcomes and Competencies

  This scheme cultivates advanced mathematical reasoning, computational accuracy, spatial representation, and statistical analysis capabilities. Learners develop precision in graph plotting, logical deduction in probability calculations, and problem-solving skills in relative motion scenarios. Practical mini-projects build research skills, data interpretation abilities, and communication skills. Furthermore, the curriculum fosters critical thinking, financial and risk literacy, teamwork through educational games, and awareness of road safety required for higher education in STEM fields, economics, and data science.

  Learning Experiences and Practical Activities

  Learners engage in active mathematical learning experiences, including participating in outdoor races with stopwatches to calculate velocity, collecting real-world traffic data, and tossing coins or rolling dice to record experimental probabilities. Practical activities include plotting displacement-time and velocity-time graphs on grid paper, calculating line gradients, measuring bounded graph areas using geometric formulas, and working out relative speed meeting times. Learners group raw data into frequency tables, calculate mean values using sum(fx)/sum(f), construct histograms with equal and unequal class widths using frequency density, draw frequency polygons, generate sample space grids, construct probability tree diagrams, execute statistical mini-projects, and play educational probability card/spinner games.

  Assessment Methods

  Formative and Diagnostic Assessment: Oral questioning, classroom board problem solving, graph plotting inspections, table construction checks, and peer feedback during paired problem solving.

  Practical and Project Assessment: Scoring rubrics for statistical mini-project presentations, histogram drawing accuracy checks, frequency polygon inspection, tree diagram layout checks, and practical probability game scorecards.

  Summative Assessment: Timed calculation tests, written sub-strand revision quizzes, a formal End-of-Term Examination Paper 1, a formal End-of-Term Examination Paper 2, and math project portfolio scoring.

  Learning Resources

  Geometrical and Calculation Instruments: Graph paper, squared workbooks, rulers, geometrical sets, T-squares, stopwatches, tape measures, and scientific calculators.

  Statistical and Data Tools: Raw data collection sheets, tally mark templates, grouped frequency worksheets, sample statistical reports, and mini-project presentation charts.

  Probability Supplies and Game Gear: Coins, six-sided dice, spinners, playing cards, event cards, probability scale charts, and tree diagram worksheets.

  Textual and Visual Media: Grade 10 Core Mathematics Curriculum Design (KICD 2025), road safety charts/posters, case study printouts, past examination papers, and official marking schemes.

• File Size:

  43.45 KB
• Length:

  43 pages
• Category:

  Schemes of Work
• Level:

  Grade 10
• Subject:

  CORE MATHEMATICS
• Posted By:

  Caleb_Peter

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