The purpose of this feasibility study was to examine the potential impact of reading digital interactive e-books on essential skills that support reading comprehension with third-fifth grade students. Students read two e-Books that taught word learning and comprehension monitoring strategies in the service of learning difficult vocabulary and targeted science concepts about hurricanes. We investigated whether specific comprehension strategies including word learning and strategies that supported general reading comprehension, summarization, and question generation, show promise of effectiveness in building vocabulary knowledge and comprehension skills in the e-Books. Students were assigned to read one of three versions of each of the e-Books, each version implemented one strategy. The books employed a choose-your-adventure format with embedded comprehension questions that provided students with immediate feedback on their responses. Paired samples t-tests were run to examine pre-to-post differences in learning the targeted vocabulary and science concepts taught in both e-Books. For both e-Books, students demonstrated significant gains in word learning and on the targeted hurricane concepts. Additionally, Hierarchical Linear Modeling (HLM) revealed that no one strategy was more associated with larger gains than the other. Performance on the embedded questions in the books was also associated with greater posttest outcomes for both e-Books. This work discusses important considerations for implementation and future development of e-books that can enhance student engagement and improve reading comprehension.

Conditional sampling of variational autoencoders (VAEs) is needed in various applications, such as missing data imputation, but is computationally intractable. A principled choice for asymptotically exact conditional sampling is Metropolis-within-Gibbs (MWG). However, we observe that the tendency of VAEs to learn a structured latent space, a commonly desired property, can cause the MWG sampler to get "stuck" far from the target distribution. This paper mitigates the limitations of MWG: we systematically outline the pitfalls in the context of VAEs, propose two original methods that address these pitfalls, and demonstrate an improved performance of the proposed methods on a set of sampling tasks.

Hillsdale Middle School summer students lined up on opposite sides of the school cafeteria. They were getting into position to have a battle, one they had been preparing for through a summer of math, reading and problem solving activities. The battle would not be between the students but between robots, which the students programmed to turn and move in specific ways by writing code. The science, technology, engineering and mathematics teacher, Jenny Stump, counted down. While the actual battles -- there ended up being multiple rounds -- only lasted a few minutes each, weeks of preparation went into building the skills necessary to execute the activity.

Hillsdale Middle School summer students lined up on opposite sides of the school cafeteria. They were getting into position to have a battle, one they had been preparing for through a summer of math, reading and problem solving activities. The battle would not be between the students but between robots, which the students programmed to turn and move in specific ways by writing code. The science, technology, engineering and mathematics teacher, Jenny Stump, counted down. While the actual battles -- there ended up being multiple rounds -- only lasted a few minutes each, weeks of preparation went into building the skills necessary to execute the activity.

In a bygone era, when our attention spans could outlast a TikTok video, point-and-click adventure games from developers such as Sierra Online and LucasArts brought intrigue, excitement and daft jokes to our new beige personal computers. These were mentally taxing, longform experiences that often provided the sharpest wit, the most endearing characters, and tales that endured endured long after the credits scrolled. Lair of the Clockwork God was always going to need to find a new take on an old and marginalised genre. But it succeeds in doing so, by cross-pollinating with platforming – and a particularly demanding type of platforming at that. If that sounds like a recipe for seething at your screen through a clenched jaw, it's testament to writer-designers Dan Marshall and Ben Ward that far more often it's a vessel for inventive puzzles and a distinctive brand of wry observational humour about game design.

The Laboratory for Artificial Intelligence is comprised of several groups performing research in different areas of Artificial Intelligence. The LAIR was formed back in the 1970s, and the core researchers from that era form the Cognitive Systems group. There are a number of other groups at OSU conducting research in Artificial Intelligence areas; while not formally part of LAIR they have overlapping interests and are sometimes part of collaborative projects. LAIR research areas often cut across groups; while each section below describes a general activity it is best to see individulal faculty members' sites for more specific information. Computation Learning Theory is concerned with developing algorithms to allow computers to make decisions and find patterns in data by observing a data (rather than through explicitly specified rules).