Digital learning environments are becoming increasingly ubiquitous as a wide range of EdTech products and services enter classrooms and households across the globe. One salient attribute of these environments is their capacity to generate large amounts of data as students interact with the technology. These data logs can help construct a detailed picture of how students work on a task and provide valuable insights into their underlying competencies. At the same time, the sheer volume of interaction data poses challenges, such as how to extract meaningful behavioral patterns from the raw data and model them to assess specific constructs. This dissertation contributes to the efforts of educational researchers and practitioners in harnessing the data generated by digital technology to support teaching and learning, with an emphasis on using interactive tasks to assess and teach problem-solving practices. The "Introduction" chapter reviewed the historical development and various lines of inquiry in problem-solving research. The chapter also provided a summary of our lab's previous work on problem-solving practices and expert decision-making. The goal is to contextualize the present research on log data-based measurement of problem-solving within the broader problem-solving research, thereby establishing its relevance and contribution to the field. The first article, "Using Prolific as a Data Collection Tool for Educational Research," explored whether Prolific, an increasingly popular online crowdsourcing research platform, could be used to collect data on college students' problem-solving performance. Specifically, the study employed an interactive problem to compare the task engagement and performance of college students recruited from the Prolific platform with those of college students enrolled in an introductory physics course in a large public university. The Mystery Gift problem challenges students to determine the weight of an unknown gift using known weights and a marked seesaw in the PhET simulation. Results show that Prolific participants performed on par with college students from the physics class in obtaining the correct solutions. Furthermore, college students who submitted incorrect answers were more likely than Prolific participants to make rushed cursory attempts to solve the problem. These results suggest that Prolific is a valid data collection platform for studying how college students solve complex, interactive problems in science and engineering domains. The second article is "Applying Log Data Analytics to Measure Problem-Solving." This study investigated the potential of log data, which recorded students' interactions with the digital task environment while solving the Mystery Gift problem, as evidence of their problem-solving practices. Building upon the validity of Prolific as a data collection tool established in the first study, a national sample of 80 US college students majoring in STEM fields was recruited via Prolific to participate in a 30-min online study. The log data was processed to reveal the sequence of test trials conducted by individual participants when solving the problem and the pauses between test trials. Our analyses revealed that taking deliberate pauses (10 secs) during problem-solving was a significant predictor of participants' problem-solving success and an indicator of specific problem-solving practices. The results highlight the value of log data in offering unobtrusive observations of students' problem-solving processes and the power of learning analytics techniques in extracting semantically meaningful features associated with specific problem-solving practices. [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://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]