Academic growth

Academic growth is the measure of student’s development between two points in time. Like calculating growth range from subtracting last year’s test score from this year’s test score to complex statistical models that account for differences in student academic and demographic characteristics.

But measuring student growth is not easy. Some many different methods and models exist, with everyone having their proper implementation. No matter how you measure growth, all methods of measuring growth depend on several factors to be valid and reliable.

India holds an important place in the global education industry. The country has more than 1.4 million schools with over 227 million students enrolled and more than 36,000 higher education institutes. India has one of the largest higher education systems in the world. However, there is still a lot of potential for further development in the education system.

India has become the second-largest market for e-learning after the US. The sector is currently pegged at US$ 2–3 billion and is expected to touch US$ 40 billion by 2017. The distance education market in India is expected to grow at a Compound Annual Growth Rate (CAGR) of around 34 percent from 2013–14 to 2017–18. Moreover, the aim of the government to raise its current gross enrolment ratio to 30 percent by 2020 will also boost the growth of distance education in India.

We need to have good assessments. These assessments should feature quality items, number of items, standards, DOK levels, P-Values, point bi-serial correlations and so forth. Additionally, there needs to be an alignment between the assessment, curriculum, and methods of instruction. Students need to be motivated and engaged, so we have some idea of the reliability of the data that is being provided to us through the assessments.

Methods to measure students’ growth

1. Gain Score Method

It measures year-to-year change by subtracting the prior year (initial) score from the current year (final) score. The gains for a teacher are averaged and compared to the overall average gain for other teachers. It’s quite easy to compute and can be used with local assessments.

2. Value-Added Models

This is frequently used for teacher evaluation that measures the teacher’s contribution in a given year by comparing the current test scores of their students to the scores of those same students in previous school years.

In this manner, value-added modeling seeks to isolate the contribution that each teacher provides in a given year, which can be compared to the performance measures of other teachers.

VAMs are considered to be fairer than the gain score model since it considers potentially confounding context variables like past performance, student status or family income status. They’re generally not used with local assessments but are intended for state accountability assessments. Typically, the calculations of these models can be a little complex, and one needs quite a bit of data for that to take place.

3. Student Growth Percentile Model

It describes a student’s growth compared to other students with similar prior test scores. Percentiles are used to rank a student’s growth compared to others, which is more a straightforward and easier-to-interpret method.

The student growth percentile allows us to fairly compare students who start at different levels with similar students. This is called “banded growth.”

Unlike VAMs, each student receives an individual percentile score. In this model, students will always be categorized as “winners” and “losers” because it’s going to be normalized around a percentile rank model. Therefore, some students will always be high percentile, and some will be low percentile.

SGPM can answer these questions:

How are the overall district and school growth relative to students in the district and throughout the state?

Is there sufficient student growth made towards meeting state standards?

Our district and site efforts leading to positive student growth and outcomes?

What student subgroups are making greater gains than others?

For teachers, student growth will help answer questions like:

• What students met (or are on track to meeting) their expected growth?
• What students are growing at a higher or lower rate academically relative to other students in the classroom/school?
• How are students growing compared across different content areas (e.g., Math and English)?
• What intervention efforts are working?

4. Effect Size Approach

It is a way of quantifying the size of the difference between two groups that is easy to calculate, understand and be used with any outcome in education.

The goal of the Effect Size is to provide a measure of the “size of the effect” or impact from instruction rather than pure statistical significance which gets confounded with effect size and sample size.

• Computer-Adaptive Approaches

These have become quite common and typically use a “Vertical Scaled Score” to show growth a single year over multiple years on the same scale. This method is ungraded and could be similar to measuring a student’s height — as a student grows on their vertical scale, they are increasing on their academic skills accordingly.

They can also overcome the limitations of fixed-form assessments (i.e., a subset of items that can’t be adjusted). In other words, adaptive assessments will adapt downward or upward in difficulty based on a student’s performance.

5. Progress Monitoring

Progress monitoring is different in that it’s typically criterion-referenced assessments. They’re efficient to administer and the data can be displayed to show the “absolute discrepancy” between where the student is performing relative to the expected target or level. The data can be graphed to measure a change in the rate of progress relative to expected growth over time.

Educators use evidence of their students’ progress every day, in formal and informal ways. Some may choose to use growth data (SGPs, value-added, gain scores) when developing their Student/School Learning Objective (SLO) for Educator Effectiveness.

Educators also use growth data to examine trends among their student population, grade levels, and content areas. Growth data can be an important piece of the data puzzle that aids instructional and local programmatic decisions.