7.3: Applying Evaluation Criteria- Rubrics, Surveys, Data Analytics, and Feedback
- Page ID
- 30712
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)- Identify tools for evaluating technology effectiveness
- Explore assessment rubrics, surveys, data analytics, and feedback mechanisms
Tools for Evaluating Technology Effectiveness
When assessing the effectiveness of educational technology, several tools can provide valuable insights. These tools help educators and administrators measure how well a technology enhances student engagement, contributes to learning outcomes, and satisfies user needs. Here are some user-friendly tools and approaches suitable for evaluating technology in the classroom.
Assessment Rubrics
Assessment rubrics are structured guidelines that help educators evaluate various aspects of technology use, such as content relevance, ease of use, and impact on learning objectives. For example a rubric may assess an educational app based on criteria like alignment with curriculum standards, user-friendliness, and the degree of interactivity. Here is an example Creative Commons rubric that you can review for your own use: TechIntegrationAssessmentRubric.pdf (Harris, et.al).
Surveys
Surveys collect feedback from teachers, students, and administrators about their experiences with a particular technology. They provide valuable qualitative data on user perceptions and preferences. A survey might ask teachers about the ease of integrating a new learning platform into their lessons or inquire about students' satisfaction with a specific educational app.
Data Analytics
Data analytics means looking at and making sense of numbers and data produced when people use technology. By doing this, you can spot trends, patterns, and connections. There are many places to access existing data on how well technology works for teaching and learning overall. To make a fair comparison, you should also collect data on how technology is directly affecting teaching and effectiveness in your own situation. Some examples of using for data analysis:
- Students Use in Online Modules: A science course has different online modules that teach different topics. Teachers can use data to see which modules students access the most and spend the most time on. By seeing the most popular modules, teachers learn which topics connect with students the best. This helps teachers reinforce concepts students like and improve modules that are less engaging.
- Time Spent on Online Tasks: A math app gives students practice exercises like word problems, quizzes, and interactive simulations. Data can show how much time students spend on each task. Teachers can see which tasks hold student attention longer. This helps teachers plan lessons, emphasize engaging activities, and make sure tasks support learning goals.
- Tracking Student Progress: Some online platforms adjust lesson difficulty to each student's ability. Data can track every student's progress, strengths, and needed improvements. Teachers can personalize teaching using this data to help struggling students with concepts they find difficult. It also helps reward student achievements.
- Collaboration: Online platforms allow students to collaborate on group projects. Data can detect patterns about which groups work well together on tasks. Noticing successful collaboration helps teachers encourage effective teamwork. It also shows individual contributions within groups, building a supportive learning environment.
- Trends in Test Performance: Data can track patterns in test scores over time - whether they improve, decline, or stay the same. Seeing performance trends helps teachers identify curriculum strengths and weaknesses. This guides teaching adjustments and interventions tailored to learning needs.
Feedback Mechanisms
Feedback mechanisms allow users to provide real-time input on their experiences with a technology. This immediate feedback helps identify areas for improvement. For example, an online learning platform may include a feature that allows students to rate the usefulness of instructional videos or provide comments on specific assignments.
Harris, J., Grandgenett, Ne., & Hofer, M. (n.d.). Technology Integration Assessment Rubric. CC NC ND 3.0.
- 7.3.1: Evidence-Based Approaches in Evaluation of Educational Technology
- In this section, look at taking that data and using it to make decisions, using evidence based approaches. Here, we simplify the process to focus on applying the basic steps to specifically evaluating the integration of educational technology in teaching and learning.