The climate in urban centers can differ significantly from the immediate surrounding areas; this can pose health risks to the elderly who have spent much of their lives in urban centers and then move to more rural areas for retirement. Therefore, there is a need to develop educational software applications for the elderly that needs to account for their life experiences aside from physiological restrictions. This research created a connected wireframe for an educational assistant to make the elderly aware of the climate differences between their urban and rural residences. To address the difficulty of recruiting vulnerable subjects, especially during the COVID-19 pandemic, we evaluated our wireframe using a combination of heuristic evaluation and a variation of the Delphi process, an expert consensus-building tool typically used for market forecasting.

As practitioners introduce new educational technologies into their classrooms, the potential for unintended outcomes from their use might arise. One such potential negative artifact is an increase in the achievement gaps between learners, where high performers tend to benefit more from newly introduced educational technologies than their peers. This phenomenon is commonly referred to as the Matthew effect. In this study, we leverage natural language processing (NLP) based transformers to introduce English language support to English as a Foreign Language (EFL) learners while they are in the writing process. A web-based application was created that uses next-word prediction and automatic reverse translation to help EFL participants in their writing.Adult English language learners from professional development language schools participated in a counterbalanced repeated measures study. To understand the presence of the Matthew effect, learners were grouped based on their self-reported EIKEN scores. Their performance according to two writing factors as well as their perceived cognitive load while using the tool were measured to establish which groups benefit the most from using the tool. This research sets the stage for understanding how emerging tools cansupport learning without exacerbating Matthew effects. These effects should beconsidered in both the development and application of educational technology.

Multiple studies have hinted at the positive effects of educational games on learner engagement. However, some games, if they exist, may not be readily usable for some lessons even though the same topics are covered. In this workshop, the participants will be introduced to Twine, an open-source development environment for nonlinear stories that requires little to no coding. Tips and tricks to making engaging stories that target desired learning outcomes derived from existing research will be shared. The participants will have the opportunity to apply these insights through a group activity where they create their stories for a lesson. They will then individually create games for their stories and share their works to the rest of the group. An online community will be created after the workshop for the participants to continue exchanging ideas or creations afterwards.

While the use of automated writing evaluation software has received much attention in CALL literature, as Frankenberg-Garcia (2019) notes, empirical research on predictive text and intelligent writing assistants is lacking. Thus, this study addressed this gap in the literature by examining the impact of Grammarly, an intelligent writing assistant that incorporates predictive text technology, on the mobile writing quality of Japanese L2 English students. Specifically, the study explored if Grammarly had a significant effect on the grammatical accuracy, lexical richness, writing fluency, or syntactic complexity of L2 students’ writing when compared to unassisted mobile writing. A total of 31 university EFL students participated in the 8-week study which utilized a counterbalanced design. Participants took part in weekly guided freewriting tasks under each writing condition (non-Grammarly and Grammarly) over the duration of the study. The descriptive statistics and results from t-tests showed that when students wrote with the assistance of Grammarly, they produced fewer grammatical errors and wrote with more lexical variation. These findings highlight the potential of predictive text and real-time corrective feedback as a way to support L2 writing, particularly among novice writers who may struggle to write effectively in the L2.

Metacognition, a set of skills essential to succeed in online learning environments, spans three distinct phases: planning, monitoring, and evaluating. An existing metacognitive tutor for developing knowledge of cognition and regulation of cognition at different problem-solving phases previously shown to be effective in an experimental setting was revisited in this research. The tool was optimized to be usable in learning management systems (LMS), which are frequently used in blended and online learning environments. It was then tested in a hybrid online class on electrical engineering offered to first-year undergraduate students. The students rated the tool while using it in an LMS and found it to be usable and improved cognition regulation significantly. The tool is cognitive domain agnostic; thus, it can be a convenient means of tutoring metacognition in online learning environments.