Cause and Effect: Predicting Student Achievement

Imagine being able to predict the success of your students, who wouldn’t want to be able to do that, right? Actions by teachers, called antecedents, may help make this dream a reality. Antecedents are “structures and conditions that precede, anticipate, or predict excellence in performance (White, 2005, p. 28). Like all lawyer television commercials this post comes with a disclaimer. There are no secrets in predicting student achievement in this post; only hypothesizing, heedfulness, and hard work will be introduced here. However, I will predict that we increase our chances of having a positive impact upon student achievement by having a better idea of the adult causes leading to the effects on student learning.

Antecedents are the actions of the adult, not the student.

Antecedents can be categorized by “causes, instructional strategies, administrative structures, and conditions for learning” (White, 2005, p. 28). Antecedents are the cause for the effect in learning or goal attainment. These are the actions of the adult, and NOT the student. For example, an antecedent is not a student’s test score, however, the antecedents (practices) of the instructor leading up to the test result could (and should) be measured and monitored. ​Antecedents are also: ​​

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Causes that correlate with effects in student behavior and achievement (results), such as classroom routines, grading procedures, and teacher-student relationships and connections. Antecedents also include conditions such as class size, technology literacy, availability of textbooks, and structures such as continued education unit (CEU) requirements, block scheduling, data teams, and prescribed data reflection times (White, 2005, p. 43).
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Consider this, if the adult is the cause, then the student’s learning is the effect. Identifying and monitoring antecedents are crucial as teachers and administrators usually know the effect (results), however, don’t always know or seek out the cause. Now that we know what antecedents are, let’s recategorize them into more teacher friendly categories of: teacher routines (causes), instructional strategies, and learning conditions (antecedents).
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What teacher routines, instructional strategies, and learning conditions do you monitor?

Think of teacher routines as classroom management. These are teacher behaviors that affect student achievement, no special training is needed, and can be replicated. For example teaching classroom procedures and establishing/following through on rules.

Instructional strategies are the tools and practices used to help how we teach. These are teacher practices that engage students in thinking, require professional development and practice to do with fidelity (White, 2005). Examples of effective instructional strategies are response to intervention, cognitive task analysis, jigsaw method, and scaffolding (Hattie, 2017).

​Finally, learning conditions create the classroom environment. They are the actions that precede, anticipate, or predict excellence in performance and can be replicated. Examples include the posting of learning goals, excellent work displayed in the classroom, teacher explaining expectations to parents, and teachers making personal connections with students. You might be wondering, what does this look like in my classroom? Here is an example of a digital classroom categorized by the antecedents addressing a SMART goal.

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Teacher Routines (Causes) Think “classroom management”	​Learning Conditions (Antecedents) Think “what my room looks like”	​Instructional Strategies Think “how I teach”
​Students contribute to shared digital rules and procedures.	​Displaying and celebrating student work in the classroom and online.	​Blended Learning and/or Project Based Learning models incorporated in teaching and learning.
Using digital, formative tools for the start of class, i.e. bell-ringers.	​ISTE (and other) standards posted on classroom walls in student-friendly language. ​Reminders of student goals and responsibilities are evident.	​Technological framework such as TPACK evident in teaching.
​Using online group generators to create student teams and groups.	​Classroom layout designed to promote the 4Cs, learning centers evident, students involved in design of classroom.	Use of digital tools to enhance research based instructional strategies.
​Using digital formative assessments for the end of class, i.e. exit slips.	​Accessibility to devices and supporting WiFi infrastructure.	​Using video for re/teaching and connecting with outside experts.

This table outlines the adult actions taken that correspond to the identified weaknesses and to the SMART goal(s) (Magana & Marzano, 2014; White, 2005).

​The idea we are going for here is to envision what success looks like, but also identify the causal factors (antecedents) that contribute to the success. This is done by focusing on a few, high leverage antecedents for improvement such as using instructional strategies greater than the 40% effect size as identified by Hattie (just make sure the routine, strategy, or condition fits your desired context of learning). We need to know the cause for the effect on learning in order to be successful. Defining and monitoring antecedents is the first step towards data triangulation, which also includes accountability and collaboration.
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Monitoring antecedents helps keep learning on target.

The use and monitoring of antecedents in an accountability system is critical for student success. In a separate post I reference a continuous improvement accountability system that includes measuring and monitoring antecedents. Whatever accountability system you choose, White (2005) suggests to “ensure that action follows analysis; that roles and responsibilities are assigned to individuals and to teams; that user-friendly timelines for data management are established; and that accountability is integrated into every data-driven decision” (pp. 29-30). 

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Note that Reeves (2011) advises collecting data around achievement data as specified in the school improvement plan. Other accountability data to monitor includes the number of data walls, students tracking their own progress, number of walk-throughs, and frequency of feedback from administrator to teacher to student to name a few (Hedgpeth, 2015). Just make sure the data collection measures are aligned back to the antecedent being monitored. The table below offers suggestions of what, when, and how to measure for accountability in a digital classroom, on the assumption of a 4C goal example of trying to increase creativity in the classroom.

What to Measure	When to Measure	​How to Measure (tools for reflection and accountability)
Frequency of taught transdisciplinary 4C skill, creativity.	​Weekly	​Teacher simple reflection using a Google Form answering “how did my students demonstrate creativity this week?”
​Use of digital tools to facilitate transdisciplinary 4C skill, creativity.	​Weekly	Teacher extended reflection using a Google Form answering targeted 4Cs questions and practices.
Type(s) of paired tech resources and processes used to achieve desired learning outcomes for creativity.	​Weekly	Observations made by peers, administrators, and coaches. Google Form reflections.
Learning artifacts demonstrating student evidence of successful transfer of transdisciplinary skill, creativity.	Monthly	Student and/or teacher digital portfolio demonstrating targeted antecedents.

Examples of ongoing and interim measures while making adjustments as necessary. Note that it is important to “name” the data collection tools and build capacity in each so teachers and leaders know what to look for and how to best provide feedback. Limit data collection tools to avoid collecting more data than necessary.
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​Triangulating antecedent data with accountability measures is completed by including collaboration to the mix. Collaboration works best in the building and district when it is “(a) built into every step of data management, (b) applied in a culture of no blame and no excuses, and, (c) unified into every data-driven decision” (White, 2005, p. 30). 
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Data triangulation consists of having a time and place to collaborate, antecedent data to review, and an accountability system for continuous improvement.

​In summary, antecedents are the cause for the effect and challenge us to take on the role of the researcher. As the researcher we constantly seek to answer if the cause is having the intended effect on learning. We categorize antecedents by teacher routines, instructional strategies, and learning conditions. Instructional strategies are unique to this group as they require professional development and practice by the adult. Combining antecedents with accountability and collaboration makes for the most effective use of data triangulation. Predicting student success may seem like wishful thinking. I know we can’t control everything, however, I do believe we get what we create or allow (Cloud, 2013). Naming and claiming antecedents while triangulating accountability measures through collaboration is sure to create a recipe for student achievement success, just like we predicted earlier. ​

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Below are the resources mentioned from today’s post should you want to learn more about the cause(s) leading to the effect(s) upon learning. In the meantime let us know which antecedents you are monitoring in your classroom in the comments below. If you found this post useful please join our Pedagogize It community and subscribe for free strategies for how to best use technology for learning. The Pedagogize It community revolves around what matters most, and that is you and the work that you do. 

• Allison, et al., (2011). Activate: A leader’s guide to people, practices, and processes. Englewood, CO: Lead + Learn Press.
• Cloud, H. (2013). Boundaries for leaders. New York, NY: HarperCollins Publishers.
• Hattie, J. (2017). 250+ influences on student achievement. Retrieved from Visible Learning Plus website.
• Hedgpeth, P., (2015). Day 3: The learning leader [PowerPoint slide]. EAD8013 Leadership in Learning. Retrieved from https://sbuniv.blackboard.com 
• Magana, S., & Marzano, R. (2014). Enhancing the art and science of teaching with technology. Bloomington, IN: Marzano Research.
• Reeves, D.B. (2011). Finding your leadership focus: What matters most for student results. New York, NY: Teachers College Press.
• White, S., (2005). Beyond the Numbers: Making Data Work for Teachers & School Leaders. Lead + Learn Press: Englewood, CO.

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