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In this work, we introduce a differentiable implementation of the local natural orbital coupled cluster (LNO-CC) method within the automatic differentiation framework of the PySCFAD package. The implementation is comprehensively tuned for enhanced performance, which enables the calculation of first-order static response properties on medium-sized molecular systems using coupled cluster theory with single, double, and perturbative triple excitations [CCSD(T)]. We evaluate the accuracy of our method by benchmarking it against the canonical CCSD(T) reference for nuclear gradients, dipole moments, and geometry optimizations. In addition, we demonstrate the possibility of property calculations for chemically interesting systems through the computation of bond orders and Mössbauer spectroscopy parameters for a [NiFe]-hydrogenase active site model, along with the simulation of infrared spectra via ab initio LNO-CC molecular dynamics for a protonated water hexamer. [ABSTRACT FROM AUTHOR]

The implementation of computer-assisted pronunciation training (CAPT) has been proven to be successful in improving learners' pronunciation abilities. Automatic speech recognition (ASR) software was used to provide mediated support to 103 pre-intermediate level students (62 males and 41 females). After experiencing a two-semester of CAPT instruction in their Freshman English course, students completed a questionnaire to assess their perceptions of and attitudes towards technology. This paper reports on the findings that examine the structural relationships using the Technology Acceptance Model (TAM). The findings indicate that students, generally, were in favor of using ASR-based pronunciation training, and although no statistically significant gender difference was found, female students appeared to view its use more favorably than were their male counterparts. The perceived effectiveness of the system, and the attitudes of students towards using it, were shown to be significantly correlated, which encourages the ongoing use of ASR-based CAPT. Based on these responses, it was established that the ASR function enhanced students' awareness of their pronunciation errors. Furthermore, they willingly engaged in individual, repetitive pronunciation exercises, allowing them to build confidence in speaking practices without fearing embarrassment in front of their peers. Recommendations were provided for EFL educators interested in implementing CAPT in EFL settings.

Ensuring equity in education is a goal for sustainable development. Among the factors that hinder equity, socioeconomic status (SES) has the highest impact on learning Mathematics. This paper addresses the issue of equity at the secondary school level by proposing an approach based on adopting automatic formative assessment (AFA). Carefully designed mathematical activities with interactive feedback were experimented with a sample of 299 students of grade 8 for a school year. A control group of 257 students learned the same topics using traditional methodologies. Part of the sample belonged to low SES. The learning achievement was assessed through pre-and post-tests to understand if the adoption of AFA impacted learning and whether the results depended on the students' SES. The results show a positive effect of the experimentation (effect size: 0.42). Moreover, the effect size of the experimentation restricted to the low-SES group is high (0.77). In the treatment group, the results do not depend on SES, while in the control group, they do, suggesting that AFA is an equitable approach while traditional instruction risks perpetuating inequalities.

The present study had two research questions to investigate whole-class promotion in primary school and pupils' achievement in literacy. The study adopted the cross-sectional survey research design. The target population was composed of all teachers and pupils in primary schools in Fako Division, in Cameroon, while the accessible population was made up of all Classes Four (Grade 4), Five and Six pupils, and 60 teachers, selected using purposive sampling techniques. The instruments for data collection were a structured questionnaire and an interview guide. The descriptive statistical tools used were frequency count, percentages, and multiple response sets while Spearman's rho test was used in testing the study's hypotheses. The findings of the study indicated that teachers' use of alternative resources has a significant effect on pupils' literacy achievement. Findings also indicated that remedial teaching has a significant effect on pupils' literacy achievement. Some recommendations included both trainees and qualified teachers to receive more training regarding alternative resources and remedial teaching.

Background: Sound learning design should be based on the constructive alignment of intended learning outcomes (LOs), teaching and learning activities and formative and summative assessment. Assessment validity strongly relies on its alignment with LOs. Valid and reliable formative assessment can be analysed as a predictor of students' academic performance, but the question is how significant its predictive power is, and what other elements can affect predictions. Objectives: Our aim was to investigate the predictive power of formative assessment for summative assessment, measuring the acquisition of LOs. Methods: We analysed formative assessment results (quizzes, homework), together with log data (video and other material use, class attendance), to determine the most influential predictors and establish a reliable predictive learning analytics model. We used the Random Forest algorithm. The model is based on the data from two university mathematical courses, delivered at different years and levels of study, incorporating 813 students in two consecutive years. Results and Conclusions: Our results show that formative assessment, together with previous summative assessment, is a stronger predictor of summative assessment results than other data on students' engagement. The study pointed to the importance of completeness and quality of data, and clear links between assessment and LOs when making predictions of student results. It suggested that predictions are less reliable for the lowest and the highest performing students. It was noted that other factors can also affect predictions, like the level of LOs, or factors not easily extracted from digital data, like the learning environment and individual students' strategies.

RobotReviewer is a tool for automatically assessing the risk of bias in randomized controlled trials, but there is limited evidence of its reliability. We evaluated the agreement between RobotReviewer and humans regarding the risk of bias assessment based on 1955 randomized controlled trials. The risk of bias in these trials was assessed via two different approaches: (1) manually by human reviewers, and (2) automatically by the RobotReviewer. The manual assessment was based on two groups independently, with two additional rounds of verification. The agreement between RobotReviewer and humans was measured via the concordance rate and Cohen's kappa statistics, based on the comparison of binary classification of the risk of bias (low vs. high/unclear) as restricted by RobotReviewer. The concordance rates varied by domain, ranging from 63.07% to 83.32%. Cohen's kappa statistics showed a poor agreement between humans and RobotReviewer for allocation concealment (K = 0.25, 95% CI: 0.21-0.30), blinding of outcome assessors (K = 0.27, 95% CI: 0.23-0.31); While moderate for random sequence generation (K = 0.46, 95% CI: 0.41-0.50) and blinding of participants and personnel (K = 0.59, 95% CI: 0.55-0.64). The findings demonstrate that there were domain-specific differences in the level of agreement between RobotReviewer and humans. We suggest that it might be a useful auxiliary tool, but the specific manner of its integration as a complementary tool requires further discussion.

Artificial intelligence (AI) for supporting second language (L2) writing processes and practices has garnered increasing interest in recent years, establishing AI-mediated L2 writing as a new norm for many multilingual classrooms. As such, the emergence of AI-mediated technologies has challenged L2 writing instructors and their philosophies regarding computer-assisted language learning (CALL) and teaching. Technologies that can combine principled pedagogical practices and the benefits of AI can help to change the landscape of L2 writing instruction while maintaining the integrity of knowledge production that is so important to CALL instructors. To align L2 instructional practices and CALL technologies, we discuss the development of an AI-mediated L2 writing technology that leverages genre-based instruction (GBI) and large language models to provide L2 writers and instructors with tools to enhance English for research publication purposes. Our work reports on the accuracy, precision, and recall of our network classification, which surpass previously reported research in the field of genre-based automated writing evaluation by offering a faster network training approach with higher accuracy of feedback provision and new beginnings for genre-based learning systems. Implications for tool development and GBI are discussed.