Computational thinking (CT) has been an increasing focus of research and practice since the seminal paper by Wing in 2006, especially in pre-college (K-12) education. Barr and Stephenson defined CT as a problem-solving methodology that can be automated, transferred, and applied across disciplines. With the challenges of fitting CT into already full curricula, teachers are interested in ways to integrate CT into learning other subjects. Unfortunately, research has shown that teachers are challenged and concerned about where to find lesson materials and how to integrate CT with specific subject areas rather than teach it standalone. To address teachers' challenges and concerns, research is needed in two directions. The first direction is to identify teachers' needs, challenges, and thoughts when seeking lesson materials to determine how to support them. Teachers can find lesson materials across multiple sources, including attending professional development programs, asking colleagues, and searching online. The existing literature suggests searching online is a particularly promising and important approach, providing time flexibility and a large amount of resources. The second direction is to investigate how to effectively integrate CT with different subject areas. This includes being able to assess student learning of CT concepts, practices, and perspectives within the context of different subjects. Towards contributing to advancing these two directions in meeting teacher challenges and concerns in where to find lesson materials and integrating CT within specific subject areas, the focus of this dissertation is on "Investigating and Supporting the Integration of Computational Thinking in K-8 Subjects Learning." The main contributions are the co-design of a tool to help teachers find lessons for CT integration into various subjects online, and techniques for assessing student learning of CT concepts, practices, and perspectives using multidimensional data in a specific subject, namely music. Among various subjects explored in the literature to integrate with CT, music programming shows promise due to its common characteristics with CT in notation, sequence, repetition, and creativity. Specifically, the contributions of this dissertation include: (1) application of a human-computer interaction (HCI) method to the co-design of a web-based tool with teachers to identify their needs and implement a web-based high-fidelity prototype for locating online lessons for CT integration with other subjects, (2) a proposed meta-data standard for educational content creators to follow to share their lesson information, (3) a data logging methodology for collecting music coding process data to reveal students' programming behaviors, (4) assessment criteria based on students' coding artifacts to assess student performance using code to compose music, (5) identification of CT practices and perspectives under a well-recognized CT assessment framework, (6) the comparison of data collection and analysis methodologies between video and process logs, and (7) two case studies on the assessment of student learning in an integrated CT music context using multi-dimensional data based on summer coding camps engaging secondary school students. The results of this dissertation demonstrate that when querying through online resource centers, teachers need filters, searching, ratings, reviews, and message boards. For lesson sharing, teachers expect to see meta-data information listed more explicitly, including name, description, thumbnail images, creator, length, number of units, subjects, grade/experience levels, coding environments, coding languages, coding devices, standard alignment, and accessibility. Our case studies on CT integration with music suggest that both content knowledge and coding knowledge can lead to better student performance. Students had difficulties using loops in this learning context, especially list loops compared to numerical loops. Finally, video data contains more information than log data, but analyzing video data is more time-consuming and thus, is not appropriate for large-scale analysis. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]