Hypothesis

Using quantitative analysis of qualitative learning goals will yield actionable results that will indicate areas of short-coming within study population.

Introduction

The aim of this project was to help identify areas of high achievement, partial achievement, and low achievement. Identification of any classification could, in theory, be utilized as an indicator for or against further learning. The assessment process was quick and meant to reflect a “snapshot” judgment of whether or not they had achieved said goal. The assessment process was quick and meant to reflect a “snapshot” judgment of whether or not they had achieved said goal.

This analysis was meant to help the teacher discover areas in which the students were lacking. This was not meant to show results toward individual students.

Methods

In order to do this a binary scoring was used and a value was assigned to each standard. Entering the value of “1” meant that this student had adequately achieved this standard; conversely, entering the value of “0” meant the opposite. Using this scoring system each student was assessed. The amount of time for assigning a 1 or 0 for each standard was no more than 10 seconds. After the total analysis was completed, 6 rows were chosen at random and appraised as to whether their results agreed with the assessors opinion.

Results

18 students were assess over a possible 330 learning goals. The results were individual scores between 0-330 with 0 representing low levels of achievement and 330 high. Individual learning standards were each given a score between 0-18 with 0 representing that no students had achieved said standard and 18 meaning all students had achieved said standard. The results are shown below using a color scale correlated to the sum total. White cells represent the highest achievable score (18) and red cells represent the lowest achievable scores. Subtotals for each column are disregarded for reasons addressed in the discussion. Each of the 6 post-analysis control tests agreed with the assessors opinion.

Discussion

The learning goals the students were assess for are broken down into 5 major categories. Each category has additional subsets for which the students are assessed. These are summarized as “Long Term Goals”, “Medium term goals” and “Short term goals”. The students were analyzed as a whole for their current level of achievement for “Medium term goals” while totals for “Long term goals” were ignored. Overall, this analysis associated a quantifiable value to each learning goal.

My impressions: While this analysis was helpful at the start of the semester to indicate areas in which classroom achievement could be addressed; there were also major short-comings of this type of analysis. First and foremost is the obvious that assigning a numerical score value to a learning goal, or any time of qualitative measure for that matter, is inherently limiting. Qualitative measurements are meant to encompass the wide variety of individuals they are meant to assess. By overlapping a binary scoring system I am cutting out a huge swathe of alternative interpretations. Secondly, while this analysis can highlight extremes (a score of 1-2 or 17-18) it does not lend any commentary for the ranges in-between. And while highlighting areas that need attention (or do not) is important, it is similarly important to bolster areas that are already developed and/or developing. Finally, this analysis was opinion based and surely effected by the errors of interpretation.

Applying this analysis over the course of the semester or perhaps multiple times per unit might serve as a good check mark, yet overall the efficacy of this analysis still seems to be as a quick baseline test. I do not recommend this as a source of hard evidence for formulating a unit of inquiry but rather as tool to quickly gather large set data.