Shuchi Grover

Computational Thinking in K–12 A Review of the State of the Field

Jeannette Wing’s influential article on computational thinking 6 years ago argued for adding this new competency to every child’s analytical ability as a vital ingredient of science, technology, engineering, and mathematics (STEM) learning. What is computational thinking? Why did this article resonate with so many and serve as a rallying cry for educators, education researchers, and policy makers? How have they interpreted Wing’s definition, and what advances have been made since Wing’s article was published? This article frames the current state of discourse on computational thinking in K–12 education by examining mostly recently published academic literature that uses Wing’s article as a springboard, identifies gaps in research, and articulates priorities for future inquiries.

Designing for Deeper Learning in a Blended Computer Science Course for Middle School Students.

The focus of this research was to create and test an introductory computer science course for middle school. Titled “Foundations for Advancing Computational Thinking” (FACT), the course aims to prepare and motivate middle school learners for future engagement with algorithmic problem solving. FACT was also piloted as a seven-week course on Stanford’s OpenEdX MOOC platform for blended in-class learning. Unique aspects of FACT include balanced pedagogical designs that address the cognitive, interpersonal, and intrapersonal aspects of “deeper learning”; a focus on pedagogical strategies for mediating and assessing for transfer from block-based to text-based programming; curricular materials for remedying misperceptions of computing; and “systems of assessments” (including formative and summative quizzes and tests, directed as well as open-ended programming assignments, and a transfer test) to get a comprehensive picture of students’ deeper computational learning. Empirical investigations, accomplished over two iterations of a design-based research effort with students (aged 11–14 years) in a public school, sought to examine student understanding of algorithmic constructs, and how well students transferred this learning from Scratch to text-based languages. Changes in student perceptions of computing as a discipline were measured. Results and mixed-method analyses revealed that students in both studies (1) achieved substantial learning gains in algorithmic thinking skills, (2) were able to transfer their learning from Scratch to a text-based programming context, and (3) achieved significant growth toward a more mature understanding of computing as a discipline. Factor analyses of prior computing experience, multivariate regression analyses, and qualitative analyses of student projects and artifact-based interviews were conducted to better understand the factors affecting learning outcomes. Prior computing experiences (as measured by a pretest) and math ability were found to be strong predictors of learning outcomes.

Assessing computational learning in K-12

As computing curricula continue to make their way into K-12 schools, the issue of assessing student learning of computational concepts remains a thorny one. This paper describes the multiple forms of assessments used in a 6-week middle school curriculum with the goal of capturing a holistic view of student learning. A key aspect of this research is the use of instruments developed and shared in prior research. Included among these were several questions used in an Israeli nationwide exam to test middle school student learning of programming in Scratch. This paper reports on the use of the curriculum in two studies conducted in a public US middle school classroom, and compares performances of these students with those reported by the Israeli Ministry of Education in their large-scale study. It also argues for multiple modes of assessment of computational learning in K-12 settings.

Using a discourse-intensive pedagogy and android's app inventor for introducing computational concepts to middle school students

Past research on children and programming from the 1980s called for deepening the study of the pedagogy of programming in order to help children build better cognitive models of foundational concepts of CS. More recently, computing education researchers are beginning to recognize the need to apply the learning sciences to develop age- and grade-appropriate curricula and pedagogies for developing computational competencies among children. This paper presents the curriculum of an exploratory workshop that employed a discourse-intensive pedagogy to introduce middle school children to programming and foundational concepts of computer science through programming mobile apps in App Inventor for Android (AIA).

A future for computing education research

Seeking to address the most important issues facing the computer education research community.

Building a virtual community of practice for K-12 CS teachers

Bringing educators together and focusing their interests toward improving computer science education in high schools.

An instructor dashboard for real-time analytics in interactive programming assignments

Many introductory programming environments generate a large amount of log data, but making insights from these data accessible to instructors remains a challenge. This research demonstrates that student outcomes can be accurately predicted from student program states at various time points throughout the course, and integrates the resulting predictive models into an instructor dashboard. The effectiveness of the dashboard is evaluated by measuring how well the dashboard analytics correctly suggest that the instructor help students classified as most in need. Finally, we describe a method of matching low-performing students with high-performing peer tutors, and show that the inclusion of peer tutors not only increases the amount of help given, but the consistency of help availability as well.

Promoting active learning & leveraging dashboards for curriculum assessment in an OpenEdX introductory CS course for middle school

Lack of teachers to teach computer science (CS) and pedagogically sound introductory CS curricula remain a significant challenge facing secondary schools attempting to teach CS. This paper describes our efforts to design and pilot an online 6-week middle/high school course using Stanfords OpenEdX platform. The pedagogy, curriculum and assessment are guided by learning theory. The course leverages OpenEdX features for contextual discussions and multiple-choice assessments that promote student learning and provide feedback. The paper reports on experiences in using instructor dashboards to identify targets of student difficulty and to aid curriculum redesign.

Assessing Algorithmic and Computational Thinking in K-12: Lessons from a Middle School Classroom

As educators move to introduce computing in K-12 classrooms, the issue of assessing student learning of computational concepts, especially in the context of introductory programming, remains a challenge. Assessments are central if the goal is to help students develop deeper, transferable computational thinking (CT) skills that prepare them for success in future computing experiences. This chapter argues for the need for multiple measures or “systems of assessments” that are complementary, attend to cognitive and noncognitive aspects of learning CT, and contribute to a comprehensive picture of student learning. It describes the multiple forms of assessments designed and empirically studied in Foundations for Advancing Computational Thinking, a middle school introductory computing curriculum. These include directed and open-ended programming assignments in Scratch, multiple-choice formative assessments, artifact-based interviews, and summative assessments to measure student learning of algorithmic constructs. The design of unique “preparation for future learning” assessments to measure transfer of CT from block-based to text-based code snippets is also described.

Multimodal analytics to study collaborative problem solving in pair programming

Collaborative problem solving (CPS) is seen as a key skill in K-12 education---in computer science as well as other subjects. Efforts to introduce children to computing rely on pair programming as a way of having young learners engage in CPS. Characteristics of quality collaboration are joint exploring or understanding, joint representation, and joint execution. We present a data driven approach to assessing and elucidating collaboration through modeling of multimodal student behavior and performance data.