Using 641 principles of economics students across four universities, the authors examine whether providing monetary incentives in a prisoner's dilemma game enhances student learning as measured by a set of common exam questions. Subjects either play a two-player prisoner's dilemma game for real money, play the same game with no money at stake (i.e., play a hypothetical version), or are in a control group where no game is played. The authors find strong evidence that students who played the classroom game for real money earned higher test scores than students who played the hypothetical game or where no game was played. Their findings challenge the conventional wisdom that monetary incentives are unnecessary in classroom experiments.

Many online learning technologies grant students great autonomy and control, which imposes high demands for self-regulated learning (SRL) skills. With the fast development of online learning technologies, helping students acquire SRL skills becomes critical to student learning. Theories of SRL emphasize that making problem selection decisions is a critical SRL skill. Research has shown that appropriate problem selection that fit with students’ knowledge level will lead to effective and efficient learning. However, it has also been found that students are not good at making problem selection decisions, especially young learners. It is critical to help students become skilled in selecting appropriate problems in different learning technologies that offer learner control. I studied this question using, as platform, a technology called Intelligent Tutoring Systems (ITSs), a type of advanced learning technology that has proven to be effective in supporting students’ domain level learning. It has also been used to help students learn SRL skills such as help-seeking and self-assessment. However, it is an open question whether ITS can be designed to support students’ learning of problem selection skills that will have lasting effects on their problem selection decisions and future learning when the tutor support is not in effect. ITSs are good at adaptively selecting problems for students based on algorithms like Cognitive Mastery. It is likely, but unproven, that ITS problem selection algorithms could be used to provide tutoring on students’ problem selection skills through features like explicit instructions and instant feedback. Furthermore, theories of SRL emphasize the important role of motivations in facilitating effective SRL processes, but not much prior work in ITS has integrated designs that could foster the motivations (i.e., motivational design) to stimulate and sustain effective problem selection behaviors. Lastly, although students generally appreciate having learner control, prior research has found mixed results concerning the effects of learner control on students’ domain level learning outcomes and motivation. There is need to investigate how learner control over problem selection can be designed in learning technologies to enhance students’ learning and motivation. My dissertation work consists of two parts. The first part focuses on creating and scaffolding shared student/system control over problem selection in ITSs by redesigning an Open Learner Model (OLM, visualizations of learning analytics that show students’ learning progress) and integrating gamification features to enhance students’ domain level learning and enjoyment. I conducted three classroom experiments with a total of 566 7th and 8th grade students to investigate the effectiveness of these new designs. The results of the experiments show that an OLM can be designed to support students’ self-assessment and problem selection, resulting in greater learning gains in an ITS when shared control over problem selection is enabled. The experiments also showed that a combination of gamification features (rewards plus allowing re-practice of completed problems, a common game design pattern) integrated with shared control was detrimental to student learning. In the second part of my dissertation, I apply motivational design and user-centered design techniques to extend an ITS with shared control over problem selection so that it helps students learn problem selection skills, with a lasting effect on their problem selection decisions and future learning. I designed a set iv of tutor features that aim at fostering a mastery-approach orientation and learning of a specific problem selection rule, the Mastery Rule. (I will refer to these features as the mastery-oriented features.) I conducted a fourth classroom experiment with 200 6th – 8th grade students to investigate the effectiveness of shared control with mastery-oriented features on students’ domain level learning outcomes, problem selection skills and enjoyment. This experiment also measured whether there were lasting effects of the mastery-oriented shared control on students’ problem selection decisions and learning in new tutor units. The results of the experiment show that shared control over problem selection accompanied by the mastery-oriented features leads to significantly better learning outcomes, as compared to full system-controlled problem selection in the ITS. Furthermore, the mastery-oriented shared control has lasting effects on students’ declarative knowledge of problem selection skills. Nevertheless, there was no effect on future problem selection and future learning, possibly because the tutor greatly facilitated problem selection (through its OLM and badges). My dissertation contributes to the literatures on the effects of learner control on students’ domain level learning outcomes in learning technologies. Specifically, I have shown that a form of learner control (i.e., shared control over problem selection, with mastery-oriented features) can lead to superior learning outcomes than system-controlled problem selection, whereas most prior work has found results in favor of system control. I have also demonstrated that Open Learner Models can be designed to enhance student learning when shared control over problem selection is provided. Further, I have identified a specific combination of gamification features integrated with shared control that may be detrimental to student learning. A second line of contributions of my dissertation concerns research on supporting SRL in ITSs. My work demonstrates that supporting SRL processes in ITSs can lead to improved domain level learning outcomes. It also shows that the shared control with mastery-oriented features have lasting effects on improving students’ declarative knowledge of problem selection skills. Regarding using ITSs to help students learn problem selection skill, the user-centered motivational design identifies mastery-approach orientation as important design focus plus tutor features that can support problem selection in a mastery-oriented way. Lastly, the dissertation contributes to human-computer interaction by generating design recommendations for how to design learner control over problem selection in learning technologies that can support students’ domain level learning, motivation and SRL.

Tangible interfaces and mixed-reality environments have potential to bring together the advantages of physical and virtual environments to improve children’s learning and enjoyment. However, there are too few controlled experiments that investigate whether interacting with physical objects in the real world accompanied by interactive feedback may actually improve student learning compared to flat-screen interaction. Furthermore, we do not have a sufficient empirical basis for understanding how a mixed-reality environment should be designed to maximize learning and enjoyment for children. I created EarthShake, a mixed-reality game bridging physical and virtual worlds via a Kinect depth-camera and a specialized computer vision algorithm to help children learn physics. I have conducted three controlled experiments with EarthShake that have identified features that are more and less important to student learning and enjoyment. The first experiment examined the effect of observing physical phenomena and collaboration (pairs versus solo), while the second experiment replicated the effect of observing physical phenomena while also testing whether adding simple physical control, such as shaking a tablet, improves learning and enjoyment. The experiments revealed that observing physical phenomena in the context of a mixed-reality game leads to significantly more learning (5 times more) and enjoyment compared to equivalent screen-only versions, while adding simple physical control or changing group size (solo or pairs) do not have significant effects. Furthermore, gesture analysis provides insight as to why experiencing physical phenomena may enhance learning. My thesis work further investigates what features of a mixed-reality system yield better learning and enjoyment, especially in the context of limited experimental results from other mixed-reality learning research. Most mixed-reality environments, including tangible interfaces (where users manipulate physical objects to create an interactive output), currently emphasize open-ended exploration and problem solving, and are claimed to be most effective when used in a discovery-learning mode with minimal guidance. I investigated how critical to learning and enjoyment interactive guidance and feedback is (e.g. predict/observe/explain prompting structure with interactive feedback), in the context of EarthShake. In a third experiment, I compared the learning and enjoyment outcomes of children interacting with a version of EarthShake that supports guided-discovery, another version that supports exploration in discovery-learning mode, and a version that is a combination of both guideddiscovery and exploration. The results of the experiment reveals that Guided-discovery and Combined conditions where children are exposed to the guided discovery activities with the predict-observe-explain cycle with interactive feedback yield better explanation and reasoning. Thus, having guided-discovery in a mixed-reality environment helps with formulating explanation theories in children’s minds. However, the results also suggest that, children are able to activate explanatory theory in action better when the guided discovery activities are combined with exploratory activities in the mixed-reality system. Adding exploration to guided-discovery activities, not only fosters better learning of the balance/physics principles, but also better application of those principles in a hands-on, constructive problem-solving task. My dissertation contributes to the literatures on the effects of physical observation and mixed-reality interaction on students’ science learning outcomes in learning technologies. Specifically, I have shown that a mixed-reality system (i.e., combining physical and virtual environments) can lead to superior learning and enjoyment outcomes than screen-only alternatives, based on different measures. My work also contributes to the literature of exploration and guided-discovery learning, by demonstrating that having guided-discovery activities in a mixed-reality setting can improve children’s fundamental principle learning by helping them formulate explanations. It also shows that combining an engineering approach with scientific thinking practice (by combining exploration and guided-discovery activities) can lead to better engineering outcomes such as transferring to constructive hands-on activities in the real world. Lastly, my work aims to make a contribution from the design perspective by creating a new mixed-reality educational system that bridges physical and virtual environments to improve children’s learning and enjoyment in a collaborative way, fostering productive dialogue and scientific curiosity in museum and school settings, through an iterative design methodology to ensure effective learning and enjoyment outcomes in these settings.