Student learning evidence can be used to support decisions and set standards.

Evidence of student learning (ESL):

ESL provides quantitative evidence that a teacher has taught in such a manner that students have learned.

The evidence for student learning will vary from one educational setting to another. Much of the educational research dealing with ESL, unfortunately, is tainted by bias introduced into studies. If there is an overriding desire to measure the extent of student learning, however, this can be accomplished. The Center for Education Assessment can assist in tailoring ESL measures to your unique situation.

Some key points associated with ESL measurement are outlined below for the consideration of educators working in this area.

Key ESL Points:

ESL Generation Elements

Process –
* A repetitive process of data collection, analysis, and reporting carried out with consistency over an extended time period
* Data on student learning characteristics that has been identified and agreed upon through a collaborative process
* Data has been collected at entry and exit points for comparative purposes
* Data has been gathered from teachers, students, and administrators

Student Data –
* Data on student learning characteristics that has been identified and agreed upon through a collaborative process
* Data on course content understanding at entry and exit from course

Faculty Data –
* Data on learning opportunities provided by faculty that has been identified and agreed upon through a collaborative process
* Data on time and resources available to support course content delivery

Administrative Data –
* Data describes the level of resources used in teaching
* Data are from a common source used for a variety of institution reporting needs

Analysis & Reporting –
* Analysis of repetitive learning opportunities provided by teachers and experienced by students. Data are aggregated consistently with periodic results reported over an extended period of time
* Aggregation of gains in learning. For example a student with 50% course knowledge upon entry and 80% course knowledge on course exit will have learned 50% more than a student with 65% of course knowledge upon entry and 80% course knowledge upon exit.

Producing Evidence

Process – Operational needs are met to ensure that student learning data are collected, validated, analyzed, reviewed, and reported on a regular periodic basis. To the extent possible data are collected and used as part of daily institution activities.

Student Data – Learning preferences are allowed to vary among individuals while enabling the aggregation of evidence at class, program, unit, and institution levels. The collection of student survey data takes place confidentially and after grades have been awarded

Faculty Data –The delivery of learning opportunities are paired with student aptitude and outcomes

Analysis & Reporting – Aggregation of data supports learning that spans multiple locations, distance learning, and dispersed programs

Data Qualities

* Institution based data has priority with the option for supplemental data collection
* Unique data definitions describe results in specific numbers and values that can be aggregated, disaggregated, analyzed, and reported as performance measurements.
* Familiar data is incorporated into evidence of student learning so that it may be collected, used, and maintained within the institution on a daily basis

Student & Faculty Core Data

Student Data – An agreed upon set of core TEAS data that is collected on each student and then aggregated across program, department, unit, and institution levels

Faculty Data – An agreed upon set of core TEAS data that is collected for each faculty member and then aggregated across program, department, unit, and institution levels

System Expansion – Core TEAS data are collected with the anticipation of future data requirements so future iterations of the system will be stable and logically consistent

Data Collection

* Data collection begins with existing data and is consolidated using a combination of computer applications such as MS Word documents, MS Excel spreadsheets, Web forms, and Client/Server systems as best fits the institution’s existing processing.
* Consolidation and reporting may be facilitated by using a single computer application such as MS Excel to provide a common denominator. This makes it easier to use data from multiple information systems. A common denominator is used to make internal data compatible with vendor systems and information systems written in lower level programming languages.

Meaningful Results

* Organize output, input, and processing logic into a cohesive framework that can be adapted within the entire institution
* Ensure that data normalization, the definition of logical relationships, and the validation of data upon entry are adequate to produce reliable reports
* Data Aggregation:
– – – Alignment of data with student and faculty profiles
– – – Systematic changes in report content by organizational level
– – – Aggregation consistent with the delivery of educational services
– – – Reports reviewed for accuracy before they are released
– – – Process reviewed and maintained for continuous improvement