In the proposed research, we investigated whether the standardized neuropsychological tests commonly used to assess attention can be used to measure students’ engagement in online learning settings. Accordingly, we employed 73 students in three clinically relevant neuropsychological tests to assess three types of attention. Students’ engagement performance, as evidenced by their facial video, was also annotated by three independent annotators. The manual annotations observed a high level of inter-annotator reliability (Krippendorffs’ Alpha of 0.864). Further, by obtaining a correlation value of 0.673 (Spearmans’ Rank Correlation) between manual annotation and neuropsychological tests score, our results show construct validity to prove neuropsychological test scores’ significance as a latent variable for measuring students’ engagement. Finally, using non-intrusive behavioral cues, including facial action unit and eye gaze data collected via webcam, we propose a machine learning method for engagement analysis in online learning settings, achieving a low mean squared error value (0.022). The findings suggest a neuropsychological test-based machine learning technique could effectively assess students’ engagement in online education. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Discussion summaries have been considered important for promoting peer interactions in online discussion forums. However, most previous research only focused on student-generated summaries and their impact on learners in the summarizing roles. Even though recent artificial intelligence (AI) progress has demonstrated the possibility of generating decent forum post summaries, little attention has been paid to the educational implications of such auto-generated forum post summaries. To further understand the perceptions and reactions of students to AI-generated forum summaries, this research used a Wizard of Oz approach to collect students’ online discussion forum log data with and without the “AI-generated” summaries. The results indicated that making an auto-generated summary available for students might not necessarily boost their interactions and engagements, especially for inactive students. However, the summary could serve as a reminder for students to participate in the discussion forum in a timely manner. Future research is needed to investigate whether the timing of providing an auto-generated summary might influence its impact. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Intelligent tutoring systems (ITSs) aim to support student learning through comprehensive adaptive features, making for costly development times—about 200–300 hours of development time per hour of instruction. This proposal outlines plans to overcome several technical challenges toward building authoring tools whereby a non-programmer can build ITSs by interactively teaching simulated students. I propose both interaction design considerations and machine-learning innovations. These include a multi-modal natural language processing mechanism that mimics student learning from narrated tutorial instruction, and an active-learning mechanism that identifies training examples likely to eliminate inaccuracies in the simulated student’s induced production rules. I propose to evaluate these features over 3 user studies and evaluate the generality of this authoring method in a final open-ended authoring study. This work aims to democratize ITS authoring by opening new authoring opportunities to non-programmers by making authoring as time-efficient and natural as human-to-human tutoring. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Computer-based learning environments (CBLEs) are often used to provide customized learning experiences for students. To enhance their effectiveness, we propose analyzing learners’ actions within CBLEs. In this study, we focus on the influence of learner mindset (fixed vs growth) on interaction patterns in a CBLE designed for teaching Python programming. Learner mindset refers to their beliefs about the malleability of their abilities. Individuals with a fixed mindset believe that their abilities are fixed, while those with a growth mindset believe abilities can be developed through learning. Using log data and pattern-mining techniques, we will identify learners’ interaction patterns and behaviour while also assessing their mindset through a questionnaire. We will compare the interaction patterns of fixed and growth mindset learners using task models to support our findings. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

GCFGlobalLearning is a non-profit organization committed to creating life-changing opportunities through an innovative virtual education project that incorporates technological tools. Our platform offers free courses to learners worldwide, available in English, Spanish, and Portuguese. Since our launch, we have welcomed millions of learners. Our current focus is on incorporating artificial intelligence tools that will enhance our learners’ experience. To achieve this, we have developed a recommender system and a learning content search and organization tool that personalizes our learners’ learning journey. Even with limited information about our learners, we can enhance their experience through the use of these AI tools. In this paper, we introduce the platform’s primary components, detail how we overcome our limited learner information scenario, and share our vision of incorporating more artificial intelligence innovations in the future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

This paper introduces innovative software for efficient English language learning that incorporates machine learning and natural language processing techniques to personalize the vocabulary acquisition process for English language learners. The software was designed to enhance extensive reading by generating English language learning materials for learners based on their interests and proficiency levels. The software begins by administering a brief and straightforward vocabulary test to learners. The test is used to identify words that may be unknown to the learners from 12,000 words. The machine-learning algorithm identifies words that are most likely to be unknown to the learner based on their test performance. The software then prompts the learner to select a topic of interest such as science or music. Thereafter, the software generates personalized English language learning materials for the learner, which contain texts with specific vocabulary related to the selected topic. The material is generated using ChatGPT. The software highlights unknown words in the text, which the learner can check using a dictionary. The software then generates new material incorporating the unknown words that the learner has checked, ensuring that the learner is exposed to a wide vocabulary in his area of interest. This process is repeated multiple times with the software generating new materials and incorporating new words that the learner has checked, thereby facilitating the efficient acquisition of new vocabulary. Through this process, the learners can engage in extensive reading, enabling them to read more in English and develop their reading skills, while simultaneously acquiring new vocabulary related to their interests. The innovative approach of the software in English language learning offers a personalized, adaptive, and efficient approach to extensive reading. This can help learners improve their English proficiency by reading in a manner tailored to their interests and proficiency levels. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Classroom observation is an effective way for teachers to improve professional development, and the analysis of student-teacher interactions is critical and significant to classroom observation. However, the traditional methods of the classroom observation are mainly based on manual coding by domain experts. Although several studies have been conducted to automate the coding and analyzing process, they are either based on audio information or video information collected from the classroom, which fails to jointly utilize multimodal information like domain experts. We thus propose a student-teacher multimodal interaction analysis system that conducts the analysis using both video and audio information and accordingly generates the informative reports based on the analysis results. A preliminary evaluation of the system validates the effectiveness of the built system and the analysis results could be further used for the evidence-based teaching behavior evaluation. The current limitations and possible optimization on the built system are discussed as well. We are planning to keep improving the system and deploy it to 1000 schools located at the rural areas in three years. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

We present a randomized field trial delivered in Carnegie Learning’s MATHia’s intelligent tutoring system to 12,374 learners intended to test whether rewriting content in “word problems” improves student mathematics performance within this content, especially among students who are emerging as English language readers. In addition to describing facets of word problems targeted for rewriting and the design of the experiment, we present an artificial intelligence-driven approach to evaluating the effectiveness of the rewrite intervention for emerging readers. Data about students’ reading ability is generally neither collected nor available to MATHia’s developers. Instead, we rely on a recently developed neural network predictive model that infers whether students will likely be in this target sub-population. We present the results of the intervention on a variety of performance metrics in MATHia and compare performance of the intervention group to the entire user base of MATHia, as well as by comparing likely emerging readers to those who are not inferred to be emerging readers. We conclude with areas for future work using more comprehensive models of learners. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Interactive simulations encourage students to practice skills essential to understanding and learning sciences. Alas, inquiry learning with interactive simulations is challenging. In this paper, we seek to identify inquiry patterns across topics and evaluate their stability with regard to common behaviors and student membership. Applying a clustering approach, we propose an encoding through which we can model students’ strategies in diverse environments. Specifically, we encode each sequence with three different levels of granularity which range from simulation-specific characteristics to simulation-agnostic features. Using this generalizable encoding, we find two clusters for each of two simulations. The formed groups exhibit similar learning patterns across environments. One systematically cycles through exploring and recording systematically over all variables. The other group explores the simulation more freely. This suggests that our feature encoding captures inherent quality of inquiry with simulations and can be used to characterize learners knowledge of productive exploration. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Item selection is the key process for computerized adaptive testing (CAT) to effectively assess examinees’ knowledge states. Existing item selection algorithms mainly rely on information metrics, suffering two issues: one is that the implicit cognitive information like relations between testing items as well as knowledge components cannot be captured by the information-based methods, and the other one is that the information-based algorithms computes item’s suitableness depending on examinees’ knowledge states which are estimated and imprecise inherently. To address these two issues, this work proposes to employ reinforcement learning technology to learn the item selection algorithm automatically in a data-driven manner. It is also able to properly capture the implicit cognitive relations between different testing items and avoid unnecessary item testing, and does not depend on examinees’ estimated knowledge states at all. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.