Search for: All records

There has been increasing interest in data enclaves in recent years, both in education and other fields. Data enclaves make it possible to conduct analysis on large-scale and higher-risk data sets, while protecting the privacy of the individuals whose data is included in the data sets, thus mitigating risks around data disclosure. In this article, we provide a post-mortem on the MORF (MOoc Replication Framework) 2.1 infrastructure, a data enclave expected to sunset and be replaced in the upcoming years, reviewing the core factors that reduced its usefulness for the community. We discuss challenges to researchers in terms of usability, including challenges involving learning to use core technologies, working with data that cannot be directly viewed, debugging, and working with restricted outputs. Our post-mortem discusses possibilities for ways that future infrastructures could get past these challenges. 

Abstract This study investigates student learning and interest within the context of a single-player, open-world game designed for microbiology inquiry. The game immerses players in the role of investigative scientists tasked with diagnosing a mysterious illness on a remote island. Ordered Network Analysis (ONA) was combined with clustering techniques to analyze in-game actions (i.e., interactions with non-playable characters, exploration, and utilization of in-game educational tools) allowing us to construct student archetypes based on the behavioral patterns of 122 middle schoolers. The analysis identified four distinct clusters of students with varying engagement patterns—two showing apparent patterns of engagement and two showing apparent patterns of disengagement. The study contributes insights into tailoring educational game designs to address disengaged or ineffective behaviors, enhancing the efficacy of game-based learning experiences. 

The artificial intelligence in education (AIED) community has produced technologies that are widely used to support learning, teaching, assessment, and administration. This work has successfully enhanced test scores, course grades, skill acquisition, comprehension, engagement, and related outcomes. However, the prevailing approach to adaptive and personalized learning has two main steps. First, the process involves detecting the areas of knowledge and competencies where students are deficient. This process also identifies when or how a student is considered “at risk” or in some way “lacking.” Second, the approach involves providing timely, individualized assistance to address these deficiencies. However, a considerable body of research outside our field has established that such deficit framing, by itself, leads to reactive and less productive strategies. In deficit-based frameworks, powerful student strengths, skills, and schemas—their assets—are not explicitly leveraged... 

The General Data Protection Regulation (GDPR) in the European Union contains directions on how user data may be collected, stored, and when it must be deleted. As similar legislation is developed around the globe, there is the potential for repercussions across multiple fields of research, including educational data mining (EDM). Over the past two decades, the EDM community has taken consistent steps to protect learner privacy within our research, whilst pursuing goals that will benefit their learning. However, recent privacy legislation may cause our practices to need to change. The right to be forgotten states that users have the right to request that all their data (including deidentified data generated by them) be removed. In this paper, we discuss the potential challenges of this legislation for EDM research, including impacts on Open Science practices, Data Modeling, and Data sharing. We also consider changes to EDM best practices that may aid compliance with this new legislation. 

Massive Open Online Courses (MOOCs) have increased the accessibility of quality educational content to a broader audience across a global network. They provide access for students to material that would be difficult to obtain locally, and an abundance of data for educational researchers. Despite the international reach of MOOCs, however, the majority of MOOC research does not account for demographic differences relating to the learners' country of origin or cultural background, which have been shown to have implications on the robustness of predictive models and interventions. This paper presents an exploration into the role of nation-level metrics of culture, happiness, wealth, and size on the generalizability of completion prediction models across countries. The findings indicate that various dimensions of culture are predictive of cross-country model generalizability. Specifically, learners from indulgent, collectivist, uncertainty-accepting, or short-term oriented, countries produce more generalizable predictive models of learner completion. 

Research into student affect detection has historically relied on ground truth measures of emotion that utilize one of three sources of data: (1) self-report data, (2) classroom observations, or (3) sensor data that is retrospectively labeled. Although a few studies have compared sensor- and observation-based ap-proaches to student affective modeling, less work has explored the relationship between self-report and classroom observa-tions. In this study, we use both recurring self-reports (SR) and classroom observation (BROMP) to measure student emotion during a study involving middle school students interacting with a game-based learning environment for microbiology educa-tion. We use supervised machine learning to develop two sets of affect detectors corresponding to SR and BROMP-based measures of student emotion, respectively. We compare the two sets of detectors in terms of their most relevant features, as well as correlations of their output with measures of student learning and interest. Results show that highly predictive features in the SR detectors are different from those selected for BROMP-based detectors. The associations with interest and motivation measures show that while SR detectors captured underlying motivations, the BROMP detectors seemed to capture more in-the-moment information about the student申fs experience. Evi-dence suggests that there is benefit of using both sources of data to model different components of student affect.