Measuring and understanding instructional processes in classrooms is essential for enhancing teaching and learning outcomes. Classroom observations provide educators with integral opportunity for professional development, fostering reflective practice and continuous improvement. However, traditional observation and coaching procedures are time-consuming, cumbersome, and expensive, requiring travel or video coding. Artificial intelligence (AI) technologies present a promising solution, enabling streamlined, efficient, and nuanced analysis of classroom observation data. This study aims to develop AI-based computer vision and multi-modal models to measure computer science (CS) instructional processes in video data and validate their accuracy. Using two K-8 CS classroom instruction videos, we tested two deep-learning approaches: 1) Convolutional Neural Network (CNN) for analyzing visual imagery, and 2) a multimodal deep learning approach that analyzed both visual and auditory information. Results showed that the multimodal approach achieved the highest accuracy. Our findings support the broader application of these methods for analyzing more intricate teaching and learning processes. We discuss the implications of the results for CS education practice and research.

Broadening participation in computer science (CS) for primary/elementary students is a growing movement, spurred by computing workforce demands and the need for younger students to develop skills in problem solving and critical/computational thinking. However, offering computer science instruction at this level is directly related to the availability of teachers prepared to teach the subject. Unfortunately, there are relatively few primary/elementary school teachers who have received formal training in computer science, and they often self-report a lack of CS subject matter expertise. Teacher development is a key factor to address these issues, and this paper describes professional development strategies and empirical impacts of a summer institute that included two graduate courses and a series of Saturday workshops during the subsequent academic year. Key elements included teaching a high-level programing language (Python and JavaScript), integrating CS content and pedagogy instruction, and involving both experienced K-12 CS teachers and University faculty as instructors. Empirical results showed that this carefully structured PD that incorporated evidence-based elements of sufficient duration, teacher active learning and collaboration, modeling, practice, and feedback can successfully impact teacher outcomes. Results showed significant gains in teacher CS knowledge (both pedagogy and content), self-efficacy, and perception of CS value. Moderating results -- examining possible differential effects depending on teacher gender, years of teaching CS, and geographic locale -- showed that the PD was successful with experienced and less experienced teachers, with teachers from both rural and urban locales, and with both males and females.

Increasingly professional development (PD) programs have been designed and implemented for pre-service and in-service teachers to acquire CS content knowledge and CS pedagogy and instructional strategies for K-12 students. This paper reports on our adaptation, implementation and research program for K-8 CS teachers across a Midwestern state. More specifically, its PD program for K-8 CS teachers consists of a summer institute with two graduate courses and a series of Saturday workshops during the subsequent academic year. This paper focuses on the two summer courses: one on CS knowledge content including computational thinking, variables, conditionals, loops, arrays, functions, and algorithms, and one instructional strategies, student pedagogy, computer-aided education resources, and community building. We report our SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of the two summer institutes involving the two courses to identify what went well and what needed improvement. This paper also reviews best practices for summer PD.

WearTec is an NSF funded project focused on activities related to wearable technologies. The goals of the project are to develop an intervention that focuses on solving real world problems and practicing the engineering design process while immersed in the innovative area of wearable technologies. Curriculum has been developed focused on youth in grades 4 to 6 to teach engineering design, computer programming, and basic circuitry. The curriculum and associated professional development are designed to encourage connections between in-school and out-of-school time instruction. Results of this pilot study suggest that the WearTec activities promote female participation in engineering technology activities. In addition, student attitudes towards STEM improved as a result of participating in the program.

Learning objects (LOs) have been previously used in computer science education. However, analyses in previous studies have been limited to surveys with limited numbers of LOs and students. The lack of copious quantitative data on how LOs impact student learning makes detailed analysis of LO usefulness problematic. Using an empirical approach, we have studied a suite of LOs, comprehensive in both the content covered and the range of difficulty, deployed to CS1 courses from 2007-2010. We review previous work on predictors of achievement and impact of active learning and feedback. We also provide a high-level overview of our LO deployment. Finally, based on our analysis of student interaction data, we found that (1) students using LOs have significantly higher assessment scores than the control group, (2) several student attributes are significant predictors of learning, (3) active learning has a significant effect on student assessment scores, and (4) feedback does not have a significant effect, but there are variables with significant moderating effects.

Learning objects (LO) have previously been used to help deliver introductory computer science (CS) courses to students. Students in such introductory CS courses have diverse backgrounds and characteristics requiring revision to LO content and assessment to promote learning in all students. However, revising LOs in an ad hoc manner could make student learning harder for subsequent deployments. To address this problem, we present a systematic revision process for LOs (LOSRP) using proven techniques from educational research including Bloom's Taxonomy levels, item-total correlation, and Cronbach's Alpha. LOSRP uses these validation methods to answer seven questions in order to diagnose what needs to be revised in the LO. Then, LOSRP provides guidelines on revising LOs for each of the seven questions. As an example, we discuss how LOSRP was used to revise the content and assessment for 16 LOs deployed to over 400 students in introductory CS courses in 2009. Lastly, although initially designed for LO revision, we briefly discuss how LOSRP could be used for assessment revision in intelligent tutoring systems.

We present a framework for the automatic annotation of learning objects (LOs) with empirical usage metadata. Our implementation of the Intelligent Learning Object Guide (iLOG) was used to collect interaction data of over 200 students' interactions with eight LOs. We show that iLOG successfully tracks student interaction data that can be used to automate the creation of meaningful empirical usage metadata that is based on real-world usage and student outcomes.

A learning object is a structured, standalone media resource that encapsulates high quality information to facilitate learning and pedagogy. In this paper, we describe our approach to design, develop, and validate learning objects for CS1. In particular, we focus on one learning object that teaches students about classes and objects. SCORM (Shareable Content Object Reference Model) standards and ACM/IEEE-CS Computing Curriculum 2001 form the basis of our design. Each learning object is self-contained and by design, the length of the content section is kept short to retain student interest. The learning object has a glossary providing definitions to key terms and a help menu. Each learning object covers a core Computer Science topic addressed by four components: (1) A brief tutorial or explanation including definitions, rules, and principles, (2) A set of real-world examples illustrates key concepts and includes worked examples and problems, models, and sample code, (3) A set of practice exercises provides important active experiences to the student, with constructive feedback to student responses, (4) A set of problems graded by the computer provides a final assessment. Our instructional design also incorporates theories of multimedia learning, providing guidance on the effective combination of text, graphics audio, and Flash animation. We also report on a pilot evaluation where students rated the learning object highly in terms of its design, usefulness, and appropriateness. We present student achievement results, comparing achievement of students participating in traditional face-to-face laboratory activities versus students using the Web-based learning object. A between-group post-test only research design showed no significant achievement difference between the two groups. Results confirm our belief that the use of modular, Web-based learning objects can be used successfully for independent learning and are a viable option for distance delivery of course components. Encouraged by these results, our project and research is continuing Fall 2004, with the development of additional learning objects and instrumentation mechanisms tracking real-time dynamic activity-based data.The "Practice Exercises" section of our "Simple Class" learning object, for example, has four exercise modules: (1) class identification, where students are asked to identify whether an item is an appropriate candidate as a class (Abraham Lincoln vs. President, for example), (2) data members and methods, where students interact with an animation (with sound) to identify the appropriate data members for a dog class, (3) dissect a class definition, where students are given code with highlighted segments and are asked to label each segment into either "class", "method name", "data member", or "method body", and (4) building a class, where students are given a heterogeneous set of data members and methods, and must pick the appropriate ones to build a class; if the selection is correct, the Java-based class will be expanded accordingly with specific Java code. For each exercise, we provide extensive real-time feedback for each response. Figure 1 shows a screen shot of one of the exercises on data members and methods.

Closed laboratories are becoming an increasingly popular approach to teaching introductory computer science courses. However, as observed in [1], “Considering the prevalence of closed labs and the fact that they have been in place in CS curricula for more than a decade, there is little published evidence assessing their effectiveness. ” In this paper, we report on how students’ performance in closed laboratories relates to their performances on a placement exam, homework assignments, course exams, and how it relates to their self-reported attitudes towards our CS1 course. This analysis provides insights to help us improve the design of our laboratories as well as other components of CS1.

An introductory CS1 course presents problems for educators and students due to students' diverse background in programming knowledge and exposure. Students who enroll in CS1 also have different expectations and motivations. Prompted by the curricular guidelines for undergraduate programs in computer science released in 2001 by the ACM/IEEE, and driven by a departmental project to reinvent the undergraduate computer science and computer engineering curricula at the University of Nebraska-Lincoln, we are currently implementing a series of changes which will improve our introductory courses. One key component of our project is an online placement examination tied to the cognitive domain that assesses student knowledge and intellectual skills. Our placement test is also integrated into a comprehensive educational research design containing a pre- and post-test framework for assessing student learning. In this paper, we focus on the design and implementation of our placement exam and present an analysis of the data collected to date.

Closed laboratories are becoming an increasingly popular approach to teaching introductory computer science courses, as they facilitate structured problem-solving and cooperation. However, most closed laboratories have been designed and implemented without embedded instructional research components for constant evaluation of the laboratories' effectiveness. As a result, it is not convenient to maintain and improve the laboratories over time so that they adapt to changing CS topics, curricula, and student needs. This article reports on an integrated framework for designing, implementing, and maintaining laboratories with embedded instructional research design. Although the activities reported here are part of our department-wide effort to cover CS0, CS1, and CS2, we focus here on the design and implementation of the labs for CS1.