Various stages of learning will have different data requirements for measuring student progress toward criteria mastery. When a student is acquiring a skill, feedback for students and teachers is imperative for preparing lessons and shaping the skill or content knowledge toward mastery. Formative assessment is critical during this stage, where teachers should gather pretest diagnostic data for analyzing individual students’ readiness to learn, preferred learning styles, appropriate methods, tasks and resources (Irving, 2007). This data gives the teacher a starting point for monitoring progress and working toward fluency, proficiency and generalization. As students begin to acquire skills, their rate of responding correctly is an indication they are fluent with material or the newly acquired ability.
More formative data is needed to assess students’ ability to use a skill fluently. Reading response rates are documented as an essential skill for academic achievement (Cates & Rhymer, 2006). An interesting study applying explicit timing as an intervention to improve reading fluency by Cates & Rhymer in 2006 revealed a measure of fluency for reading rate that can be used by teachers collecting data for this skill. The research shows improved fluency in math and writing when students are aware of being timed. By applying explicit timing to reading, teachers can take data on fluency with a built-in intervention. That is efficient!
Fore, Boon, Lawson & Martin (2007)’s discussion of curriculum-based measurement conclude that CBM is an easy way to collect data on student performance with formative monitoring of basic skills. Although using computerized assessment can be used for measuring fluency in reading, spelling, and writing, research supports its use in monitoring progress in math skills in particular (Fore et. al., 2007). Using these programs, teachers can collect data on rates like the number of digits computed correctly per minute without having to record duration data while students complete tasks.
Hess and Mehta (2013) recommend a wider range of data be used for assessing students’ proficiency than the traditional approach of using test scores. The measurement should match the problem and should therefore include items like student writing samples and program evaluation data (outcome). Using data in classrooms to determine proficiency equates to internal accountability and problem solving, which again using data for instructional decision-making. Teachers collect data each time they administer summative assessments to determine criteria or content mastery. Hess & Mehta (2013) propose teachers collect more operational and granular data for determining enrichment resource requirements, personnel decisions, program effectiveness and so forth. While measuring student proficiencies, teachers are not just collecting data to ensure students are making progress and becoming proficient, but also how to solve problems if they are not.
In the last phase of learning, teachers must probe their targets to determine if a skill is generalized across new settings or applications. Another form of data for classroom use is probe data. A teacher reviews foundational or already mastered skills with students and should routinely gather information about students’ abilities to perform. For example, in reading, when a student is given a new text, the teacher can determine if he or she can use word attack or context clues for comprehension. Fluency data can also be collected intermittently during this stage and measured against previous performance to ensure the skills in maintained and generalized.

References
Cates, G. L. & Rhymer, K. N. (2006). Effects of explicit timing on elementary students’ oral reading rates of word phrases. Reading Improvement, 43(3), 148-156.
Irving, K.E. (2007). Formative assessment improves student learning. NSTA Reports!, 6.
Fore, C. I., Boon, R. T., Lawson, C. S., & Martin, C. (2007). Using curriculum-based measurement for formative instructional decision-making in basic mathematics skills. Education, 128(2), 324-332.
Hess, F. M., Mehta, J. (2013). DATA: No deus ex machina. Educational Leadership, 70(5), 71-75.