Assessment Design & Analysis


Computational Thinking Assessment

Purpose: Measure students' understanding of computational thinking concepts

Target Audience: Grades 4-6

Project Description: This 14-item multiple-choice assessment instrument is designed to measure students’ knowledge of basic computer science and coding principles. These were developed based on computational thinking concepts --sequences, loops, event, conditional, variables, parallelism Brennan & Resnick, 2012; CSTA, 2017), and computer science practices (debugging of errors belonging to one of the six concepts).

Spatial Reasoning + Computational Thinking Assessment

Purpose: Measure students' cognitive performance with spatial reasoning and computational thinking

Target Audience: Grades K-3

Project Description: This spatial reasoning + computational thinking test of 12 items was developed in consideration of two dimensions: computational thinking and mental rotation. The questions were developed in terms of three learning objectives related to computational thinking: 1) identifying the meanings of the codes, 2) predicting the outcomes of carrying out the codes, and 3) fixing the codes when the intended outcome is not achieved (debugging). Additionally, the questions were divided into two types in terms of whether it needed a mental rotation or not.


Purpose: Measure understanding of AI concepts and applications

Target Audience: Grades 6-8

Project Description: This knowledge test set consists of 20 multiple-choice items assessing students' understanding of AI concepts and applications, specifically natural language processing, machine learning, and computer vision. The expected time to take this test is 20 min. It was designed to measure students' knowledge before and after a pedagogical intervention about project-based AI instructions.

Purpose: Measure students' dispositions about learning and using AI

Target Audience: Grades 6-8

Project Description: This survey questionnaire asks middle schoolers' attitudes about learning and using AI-enabled applications. The survey consists of 14 items in 5-point Likert scale, soliciting students' responses on their current and expected abilities with AI as well as perceived task values (utility, interests, and personal importance) attached to learning AI.