The effectiveness of instructional decisions depends on the quality of the sources of data related to students’ academic success and unique struggles. In some situations, such as the start of a new school year, finding such sources can be challenging, and the results of the previously conducted standardized tests and exams often come into play (Ledoux, 2016). Many experienced teachers find the process of gathering and synthesizing data to provide the basis for instructional decisions too difficult to approach or simply feel unprepared to decide on the potential helpfulness of some sources of information about students (Filderman & Toste, 2018). Thus, having no information from recent assessments, teachers are supposed to get the maximum out of what they already know about students to make appropriate choices.
Even without recent student assessment data, specialists in the field of education should start every new school year by making a series of decisions requiring proper analytical skills and good professional judgment. The problem is that it is essential to find enough sources of accurate and non-contradictory information about students and school activities (Filderman & Toste, 2018). Therefore, to make the first decisions based on appropriate evidence, it may be important to ensure the objectivity of data sources prior to using them.
To inform my very first instructional decisions at the start of the new school year, I will make use of multiple available sources of data, paying special attention to the results of last year’s standardized assessments, data from students’ personal records, and last year’s attendance statistics. The main reason to give preference to these specific sources is that they do not offer biased or subjective information that is based on opinions instead of facts. All three sources allow accessing data that have been documented and checked by qualified professionals. In contrast to that, despite being worthy of attention, data on children’s current skills and knowledge coming from students’ parents can sometimes and be in conflict with professionals’ conclusions and disconnected from reality.
The three selected sources of information about students will support my start-of-the-year decisions by shedding light on the need for individualized educational interventions and special services. For instance, data from last year’s reliable standardized assessments will be used to identify struggling learners and set realistic expectations when working with them (McAfee, Leong, & Bodrova, 2016). Next, information from pupils’ personal records (medical concerns, language barriers, any family emergencies and changes, etc.) will be used to differentiate instruction for subgroups with specific learning, physical, and psychological needs, such as English learners or disability students (“COE lesson plan template,” n.d.). Apart from that, attendance sheets will provide extremely important units of data by pointing to the students that have missed many school days for medical or other reasons. Having access to this information, I will be ready to provide these students and their families with specific recommendations regarding how to keep up with the rest of the class.
To propel students’ academic progress to the next level and provide them with timely and relevant feedback, it is critical to learn the basics of the proper use of data in the educational process. However, using data to support a productive teacher-student dialogue is not always simple in early childhood education settings. From the considerations of effectiveness and appropriateness, young students’ age-related cognitive and psychological characteristics require close attention when designing plans on how to use data.
First of all, when using data with young children, it is of utmost importance to make allowances for their perspectives on self-assessment and make this process as comfortable and non-harmful as possible. For instance, the video by Fairfax Network (n.d.) illustrates how teachers check their initial assumptions about academic progress by asking third graders to evaluate their knowledge and openly share their results. As an early childhood educator, I would avoid relying on this approach to data collection on a regular basis; instead, I would provide students with the opportunity to communicate their knowledge gaps in a personal conversation. From my experience with younger students, the confidentiality of student data is drastically important since many children may find it very stressful to become the center of attention if they differ from peers in terms of knowledge or skills.
Another thing that deserves special attention when using data with young children is making the entire process aligned with practice-oriented goals and analyzing data to understand the “big picture” of the student’s development. Data collection and analysis can be resource-consuming, and it does not seem appropriate to conduct such procedures just to define very specific skills that the child should polish to improve his or her grades. Some institutions even initiate data collection without clear purposes in mind, which does not add to students’ success (Yazejian & Bryant, 2013). From my perspective, it is critical to draw links between specific facts and units of data to see patterns that may sometimes point to unobvious physical and mental health issues hindering young students’ development. Using this approach to data, early childhood educators become able to provide meaningful recommendations to children’s families, including the need for health examinations, instead of just offering the list of topics to be revised. Prioritizing such links when using data in young students is essential since healthy cognitive, physical, and psychological development largely defines all children’s future accomplishments.
COE lesson plan template. (n.d.).
Fairfax Network. (n.d.). Best practice: Data-driven dialogues [Video file]. Web.
Filderman, M. J., & Toste, J. R. (2018). Decisions, decisions, decisions: Using data to make instructional decisions for struggling readers. Teaching Exceptional Children, 50(3), 130-140.
Ledoux, M. (2016). What data-driven improvement really looks like. Principal, 40-41.
McAfee, O. D., Leong, D. J., & Bodrova, E. (2016). Assessing and guiding young children’s development and learning (6th ed.). Boston, MA: Pearson Education.
Yazejian, N., & Bryant, D. (2013). Embedded, collaborative, comprehensive: One model of data utilization. Early Education & Development, 24(1), 68-70.