Thoughts
Data is becoming more crucial in practically every part of our lives in today's data-driven society. It is changing the way we do business, make policy decisions, and live our daily lives. Data is guiding our decisions, both professionally and personally. Being data-driven implies that we use data to make wise decisions and produce favorable results. We can make schools and the world more informed and effective by embracing the power of data.
How do we collect useful data? Using evaluations that are associated with the learning objectives or goals, we may collect useful data. It is critical that the evaluations give useful data that may be used to inform instruction and decision-making. Utilizing evaluations that are not aligned with the learning objectives or goals can lead to data overload, inaccurate data, misguided instruction, wasted time, and poor decision-making.
The term "assessment” in the classroom refers to any and all activities in which educators evaluate their students' knowledge and skills in relation to established benchmarks and other learning objectives in order to determine whether or not their students have mastered a certain topic (Powell, 2012). There are a wide variety of assessment methods used today, including multiple-choice exams, projects, oral presentations, essays, instructor observations, and online quizzes. We are able to alter and direct student learning in accordance with the outcomes of the assessments, which allows us to determine each student's strengths and shortcomings. Through the use of student evaluation, teachers are able to determine the extent to which their lessons are successfully conveyed to their students by tying a student's performance to predetermined academic goals. In this way, teachers can determine whether or not their students are learning from their instruction. This gives teachers the ability to permanently implement pedagogical methods that have proven to be beneficial while also modifying strategies that have been shown to be ineffectual over time.
Decision-Driven Data Collection vs. Data Driven Instruction
Decision-driven data collection is the process of acquiring information in order to make a well-informed educational decision, such as whether to go on to the next lesson or reteach the current lesson in a new way (Wiliam & Harrison, 2019).
Data-driven instruction bases lesson decisions on information gathered from the students being taught. With this strategy, teachers evaluate the results of numerous tests, as well as student work and other sorts of data, to determine their students' strengths and weaknesses, and then personalize their classes to match those individual needs.
Both data-driven decision-making and data-driven teaching share a common goal: to use collected information to enhance classroom practices. Both methods, despite their differences, rely on data collection and analysis to aid in decision making and the enhancement of teaching.
Decision-driven data collection typically entails gathering and analyzing information with the express purpose of informing decisions about education, such as pinpointing problem areas in the curriculum or gauging the success of a new teaching method. On the other hand, data-driven instruction requires routine data collection and analysis to track student growth and make instructional adjustments accordingly.
"Driven by Data," written by Paul Bambrick-Santoyo in 2010, provides a comprehensive framework for implementing data-driven learning in schools. The four pillars of the framework are (1) meaningful assessment; (2) analysis to determine the root causes of both strengths and shortfalls; (3) action to teach students what they need to know; and (4) culture to foster a climate in which data-driven instruction can flourish.
When it comes to the four cornerstones of Bambrick-Santoyo's framework I couldn't agree more. To me, information is key, and I have faith that this framework will aid educators in identifying and addressing individual students' needs, which will lead to more effective teaching and learning. Yet the strategy can only work if everyone follows through and does their part.
The framework's heavy reliance on data from exams, which can result in an overemphasis on testing and take away from other crucial components of teaching and learning like creativity and critical thinking, is one drawback to be mindful of if the framework is applied. However, the framework may produce an excessive amount of data, which teachers may find challenging to manage and use effectively, particularly if they do not receive enough help and training in what to do with the data.
Computer-Based Assessment
The introduction of computer-based assessment has dramatically changed the way that tests are administered and graded, as well as how teachers deliver feedback to their students. The ability to create questions at random, set time limits for test takers, and disable features of the testing equipment all contribute to a more secure and personalized testing experience. Yet, it necessitates that educators be trained in its management and troubleshooting. It also requires a solid technological foundation, and there may be glitches during the testing period.
Nonetheless, there is a common fear that cheating will occur when taking tests online. In order to stop students from cheating on computer-based tests, one must first define cheating and establish what is expected of them. Instructors should make it a priority to foster a culture of academic integrity among their students and inform them of the consequences of engaging in dishonest academic behavior. Furthermore, introducing techniques to avoid cheating such as shorter, more frequent quizzes, time limits on assessments, randomize test questions, utilize subjective questions where possible, and employ custom browsers that prohibit internet searches are all useful strategies (Feeney, 2017).
Conclusion
In conclusion, data-driven decision making is vital now that people and corporations can collect and analyze more data. Cutting-edge analytics technologies may collect, process, and analyze data for improved decisions and results. Data-driven decisions can help firms find new markets, improve operations, and satisfy customers. Data-driven solutions can improve healthcare outcomes, cost, and service quality. Data-driven government and public policy decision making can improve policy, services, and safety. Data-driven school administrators can also choose curriculum, teacher professional development, and resource allocation. Most importantly, assessment and other types of data assist educators in determining the needs of their students, allowing them to better meet those needs and assist those students in achieving success.
References
Bambrick-Santoyo, P. (2010). Driven by data A practical guide to improve instruction. Jossey
Bass.
Feeney, J. (2017, July 11). How to Prevent Cheating During Online Tests. Schoology.
Retrieved March 18, 2023, from https://www.schoology.com/blog/how-prevent
cheating-during-online-tests
Powell, S. D. (2012). Your introduction to education: Explorations in teaching. Pearson.
Wiliam, D., & Harrison, C. (2019, November 8). NE710 planning for learning - examples of
decision-driven data collection. YouTube. Retrieved March 18, 2023, from
https://youtu.be/1MMDLZxJz0k
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