2 nd sem Assessment for learning

Module 1 - Assessment and Evaluation (10 hrs)

https://youtu.be/25zQr6Piro0?si=QxmXYsDUORD-Jv60

1.1 Conceptual overview of Assessment and Evaluation –

https://youtu.be/amYwMbp2lCw?si=FgSCz0GCAp-zduQo

classification- oral, written

and performance evaluation https://youtu.be/UFv4Gq2kF0Q?si=i6O56-_XCfL_N5Ty

- formative and summative,

objective based and

competency based,

https://youtu.be/0hVuOP1uxkY?si=kNK9Rz4A8NZP2Mmg

norm and criterion referenced,

https://youtu.be/5NrU-4otCDs?si=yEsg79qcP7gEN67h

product and process

evaluation

https://youtu.be/6mKqpjSjtZQ?si=80hz46KJyioKvqZd

1.2 Functions of Assessment-assessment for learning and assessment of learning.

https://youtu.be/G9O1NDWFJ-s?si=4MRdHbRGfQBVnp-f

1.3 Qualitative and quantitative aspects of evaluation

https://youtu.be/BhmQnBYtANQ?si=s0NfzmyB5LWZg-GN

1.4 Technology enabled Testing- conceptual overview

https://youtu.be/N853dcXU9W8?si=haeOFKHGel-Alh-2

1.5 Differentiated assessment-Meaning and significance

https://youtu.be/RUET8VOC1cw?si=A6klFDiDoKwaWvk4

Module5 : Statistics in Education

5.1 Need and importance of statistics in education

https://youtu.be/1YSNMMDlMhk?si=eIBlxXaaYYCt8yRt

5.2 Classification and tabulation of data –need and procedure- Graphical

representation of data - bar diagram, histogram,

https://youtube.com/shorts/wRBTSFFHm_k?si=BCsljHfSzCs3StxG

https://youtu.be/eRraWuRJH8k?si=ZFyZO2agiuH3gbVR

https://youtu.be/uFS65aJ8Lr8?si=J7mhY5jfGFQVpnkq

pie diagram

https://youtu.be/zQwg37yIo48?si=QRAOgrgWnCSKeEmI

, frequency polygon,

frequency curve,

https://youtu.be/lDc5Qmvn0d4?si=6yBbUm_c6lDikVDf

cumulative frequency curve.

https://youtu.be/YWzrChiLyQ8?si=yHVeEXzawum7Jl5u

5.3 Statistical methods of analysis: - Measures of central tendency - mean,

https://youtu.be/anxzyI201UE?si=55xB8S1IvR1O5ClS

median

https://youtu.be/fq_A6COTehQ?si=cAlI4gVuX2y37P2z

and mode.-

https://youtu.be/hBNIdIlBrTQ?si=8Rpgdcx-NwxlnY3I

https://youtu.be/KgM_J8engQY?si=HeDo16NYkQy6MrI8

https://youtu.be/bzCNzdHRweA?si=B4D_OaJrLnQSXd3S

Measures of variability–range

https://youtu.be/0tZFBWr4PHY?si=-Ep30ZA1RClQQ1UB

and standard deviation

https://youtu.be/5nDngJiz_Dg?si=C-YPbIw2VmfJrXZm

5.4 Measures of relationship - concept of correlation,

https://youtu.be/OstcOhepklw?si=tySqWbu0nR3aF6u4

https://youtu.be/AGpLcWYSBN0?si=HxF3fPAJs_T6Dyz1

types of correlation, coefficient

of correlation,

https://youtu.be/R5gnPH0O4qU?si=GWe_wVf14oXuqCK6

Spearman’s rank order correlation.

https://youtu.be/Pb3937zZmNc?si=j3riyz2ZJLNHfRJs

[12/03, 11:42 am] Raji K Paul: https://youtu.be/R5gnPH0O4qU?si=l5ERh76XKcyKc26P

[12/03, 11:42 am]

: Karl Pearson’s coefficient of correlation

Percentile and percentile ranks

https://youtu.be/2TTEIptnpn0?si=AbKcCSEGCYjmXhl6

https://youtu.be/asK2xIlP3wQ?si=vCbDnwiFSDinFeva

5.5 Normal distribution -

normal probability curve and its characteristics, Skewness,

Kurtosis

https://youtu.be/5sEmkmGOteg?si=NFW1PU_woubT_ejy

5.1 Need and Importance of Statistics in Education

Q1: What is the importance of statistics in education?

A: Statistics helps educators analyze student performance, evaluate teaching methods, and improve decision-making. It also aids in educational research, assessment, and curriculum planning.

Q2: Why is statistical analysis necessary in education?

A: Statistical analysis allows educators to identify patterns, measure learning outcomes, and make data-driven decisions to improve education quality.

Q3: How does statistics help in educational research?

A: Statistics help in designing experiments, analyzing results, and drawing conclusions to improve teaching strategies and student learning.

5.2 Classification and Tabulation of Data

Q4: What is the need for classification and tabulation of data in education?

A: Classification and tabulation help in organizing raw data, making it easier to analyze and interpret for educational decision-making.

Q5: What are the different methods of graphical representation of data?

A: The main types are:

Bar Diagram – Represents categorical data using rectangular bars.

Histogram – Represents frequency distribution of continuous data.

Pie Diagram – Shows data as proportional segments of a circle.

Frequency Polygon – Represents data using line segments.

Frequency Curve – A smooth curve representing data trends.

Cumulative Frequency Curve (Ogive) – Shows cumulative frequencies in a dataset.

5.3 Statistical Methods of Analysis

Q6: What are the measures of central tendency?

A: The measures of central tendency include:

Mean (Average) – The sum of all values divided by the number of values.

Median – The middle value in an ordered dataset.

Mode – The most frequently occurring value in a dataset.

Q7: Define measures of variability.

A: Measures of variability indicate the spread or dispersion of data and include:

Range – The difference between the highest and lowest values.

Standard Deviation – A measure of how much data deviates from the mean.

5.4 Measures of Relationship

Q8: What is correlation?

A: Correlation measures the relationship between two variables. It shows whether they move together (positive correlation), move in opposite directions (negative correlation), or are unrelated (zero correlation).

Q9: What are the types of correlation?

Positive Correlation – Both variables increase or decrease together.

Negative Correlation – One variable increases while the other decreases.

Zero Correlation – No relationship between variables.

Q10: What is Spearman’s Rank Order Correlation?

A: It is a non-parametric measure of correlation that assesses the relationship between two ranked variables.

Q11: What are percentiles and percentile ranks?

A: A percentile is a value below which a certain percentage of data falls. A percentile rank indicates a student's relative position compared to others in a group.

5.5 Normal Distribution

Q12: What is a normal probability curve?

A: It is a bell-shaped curve that represents a normal distribution where most values cluster around the mean, with symmetrical tails on both sides.

Q13: What are the characteristics of a normal probability curve?

Symmetrical around the mean.

Mean, median, and mode are equal.

68% of data falls within one standard deviation from the mean.

The curve approaches but never touches the x-axis.

Q14: Define skewness and kurtosis.

Skewness measures the asymmetry of a distribution. Positive skew means a longer right tail, while negative skew means a longer left tail.

Kurtosis measures the "peakedness" of a distribution. High kurtosis means a sharp peak, while low kurtosis means a flatter curve.