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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]

The task of natural language parsing can naturally be embedded in the maximum-margin framework for structured output prediction using an appropriate joint feature map and a suitable structured loss function. While there are efficient learning algorithms based on the cutting-plane method for optimizing the resulting quadratic objective with potentially exponential number of linear constraints, their efficiency crucially depends on the inference algorithms used to infer the most violated constraint in a current iteration. In this paper, we derive an extension of the well-known Cocke–Kasami–Younger (CKY) algorithm used for parsing with probabilistic context-free grammars for the case of loss-augmented inference enabling an effective training in the cutting-plane approach. The resulting algorithm is guaranteed to find an optimal solution in polynomial time exceeding the running time of the CKY algorithm by a term, which only depends on the number of possible loss values. In order to demonstrate the feasibility of the presented algorithm, we perform a set of experiments for parsing English sentences. [ABSTRACT FROM PUBLISHER]

Comparing XML documents with XML grammars, also known as XML document and grammar validation, is useful in various applications such as: XML document classification, document transformation, grammar evolution, XML retrieval, and the selective dissemination of information. While exact (Boolean) XML validation has been extensively investigated in the literature, the more general problem of approximate (similarity-based) XML validation, i.e., document–grammar similarity evaluation, has not yet received strong attention. In this paper, we propose an original method for measuring the structural similarity between an XML document and an XML grammar (DTD or XSD), considering their most common operators that designate constraints on the existence, repeatability and alternativeness of XML elements/attributes (e.g., ?, ∗ , MinOccurs , MaxOccurs , etc.). Our approach exploits the concept of tree edit distance, introducing a novel edit distance recurrence and dedicated algorithms to effectively compare XML documents and grammar structures, modeled as ordered labeled trees. Our method also inherently performs exact validation by imposing a maximum similarity threshold (minimum edit distance) on the returned results. We implemented a prototype and conducted several experiments on large sets of real and synthetic XML documents and grammars. Results underline our approach’s effectiveness in classifying similar documents with respect to predefined grammars, accurately detecting document and/or grammar modifications, and performing document and grammar relevance ranking. Time and space analysis were also conducted. [ABSTRACT FROM AUTHOR]