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If the past two years have proven anything, it is that students today hold a tremendous capacity to adapt to incredibly difficult learning environments. This paper explores student learning experiences in one such learning environment, the period of online learning that occurred between January and March 2021 due to COVID-19-related school closures. Investigating the effectiveness of the digital learning platform Padlet in terms of student engagement with the Latin curriculum and its cultivation of Latin comprehension skills, this case study of Year 8 students in a state-maintained girls' grammar school demonstrates the value of collaborative, discussion-based approaches to remote teaching. The value of digital learning platforms in our post-pandemic educational environment is demonstrated in its potential to bridge the communicative gap felt by many students when taught online, simulating the lively classroom climate which so often generates exciting and effective learning opportunities for each student. As this paper demonstrates, digital learning platforms hold the potential not only to assist educators in coping with the challenges of remote learning, but also to extend student learning in ways once considered unimaginable beyond the realms of the physical classroom. As a result, this paper argues that the position of such platforms in the post-pandemic classroom must be advocated and supported further. [ABSTRACT FROM AUTHOR]

Equation discovery, also known as symbolic regression, is a machine learning task of inducing closed-form equations from data and background knowledge. The latter takes various forms. Domain-specific knowledge can constrain the space of candidate equations to those that make sense in the scientific or engineering domain of use. Cross-domain knowledge, on the other hand, imposes general rules for model acceptability, such as parsimony, understandability, or consistency of the equations with the dimensional units of the variables. In this paper, we propose using attribute grammars to ensure the induced equations' dimensional consistency. Attribute grammars are flexible enough to combine cross-domain knowledge on dimensional consistency with domain-specific knowledge expressed as a probabilistic context-free grammar. At the same time, we show that attribute grammars can be efficiently transformed into probabilistic context-free grammars for equation discovery with existing algorithms. Finally, we provide empirical evidence that attribute grammars ensuring dimensional consistency of equations can significantly improve the performance of equation discovery on the standard set of a hundred Feynman benchmarks. • Attribute grammars elegantly express dimensionally consistent expressions. • Efficient combining of dimensional analysis with other background knowledge. • Generate equations by transforming into probabilistic context-free grammar. • Discovered 60% more benchmark equations by accounting for dimensional consistency. [ABSTRACT FROM AUTHOR]

The ability to translate unseen passages is a skill tested in both Latin A-level and the International Baccalaureate (IB) Diploma. Higher IB candidates are expected to translate a passage of 105–125 words of Latin poetry (in this case, Ovid's Metamorphoses) as the first of their externally marked papers. The International Baccalaureate Organization (IBO) uses unseen passages to 'measure the [students'] ability to understand and translate texts in the original language' (2014, p. 25). The passages are marked according to two criteria: (a) meaning; (b) vocabulary and grammar. In order to access the highest grades, the students must provide a 'logical translation [in which] errors do not impair the meaning' and 'render vocabulary appropriately and grammar accurately and effectively' (IBO, 2014, pp. 28–9). [ABSTRACT FROM AUTHOR]

Product reviews are of great commercial value for online shopping market. The identification of customer opinions from product reviews is helpful to improve the marketing decisions of customers, sellers and producers. This paper proposes a novel framework for summarizing customer opinions from product reviews. Firstly, our framework identifies grammatically and semantically meaningful phrases which contain product attributes and their corresponding opinions from original product reviews by using grammar rules and the latent Dirichlet allocation (LDA) model. Secondly, our framework generates readable and simple summaries from the identified phrases automatically by using the deep neural network. The summaries provide users the valuable opinions on product attributes. Moreover, our framework provides an interactive mode for users to choose product attributes which they are interested for generate personalized summaries to help users focus on the most concerned opinions. Experimental results on six datasets demonstrate effectiveness of our framework. [ABSTRACT FROM AUTHOR]