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Given the increasing need for skilled workers in science, technology, engineering, and mathematics (STEM), there is a burgeoning interest to encourage young students to pursue a career in STEM fields. Middle school is an opportune time to guide students' interests towards STEM disciplines, as they begin to think about and plan for their career aspirations. Previous studies have shown that detectors of students' learning, affect, and engagement, measured from their interactions within an online tutoring system during middle school, are strongly predictive of their eventual choice to attend college and enroll in a STEM major (San Pedro et al., 2013; 2014). In this study, we extend prior work by examining how the constructs measured by these detectors relate to the decision to participate in a STEM career after college. Findings from this study suggest that subtle forms of disengagement (i.e., gaming the system, carelessness) are predictive and can potentially provide actionable information for teachers and counselors to apply early intervention in STEM learning. In general, this study sheds light on the relevant student factors that influence STEM participation years later, providing a more comprehensive understanding of student STEM trajectories. 

There are several educational games and tools that teach program- ming. However, very few offer a deep understanding of Computer Science concepts such as Abstraction, Modularity, Semantics, and Debugging. We present May’s Journey, an educational game that supports learning of basic programming concepts, where players solve puzzles and interact with the environment by typing in a cus- tom programming language. The game design seamlessly integrates learning goals, core mechanics, and narrative elements. We discuss how we integrate the CS concepts mentioned above using game mechanic metaphors. 

Research on non-cognitive factors has shown that persistence in the face of challenges plays an important role in learning. However, recent work on wheel-spinning, a type of unproductive persistence where students spend too much time struggling without achieving mastery of skills, show that not all persistence is uniformly beneficial for learning. For this reason, it becomes increasingly pertinent to identify the key differences between unproductive and productive persistence toward informing interventions in computer-based learning environments. In this study, we use a classification model to distinguish between productive persistence and wheel-spinning in ASSISTments, an online math learning platform. Our results indicate that there are two types of students who wheel-spin: first, students who do not request any hints in at least one problem but request more than one bottom-out hint across any 8 problems in the problem set; second, students who never request two or more bottom out hints across any 8 problems, do not request any hints in at least one problem, but who engage in relatively short delays between solving problems of the same skill. These findings suggest that encouraging students to both engage in spaced practice and use bottom-out hints sparingly is likely helpful for reducing their wheel-spinning and improving learning. These findings also provide insight on when students are struggling and how to make students' persistence more productive.