Automated Learning Applied to Functional Argument Identification

This paper reports experiments on applying machine learning to identifying functional arguments of verbs such as logical subject. In particular, it is shown that using decision trees for functional arguments identification is beneficial. The paper also argues that linguistically-motivated features gathered from a large corpus can capture functional information

Preventive behaviors and attitudes of the children as active participants in road traffic

In Romania, the number of pedestrian fatalities, in 2007, was 1.113 and and the number dropped considerably, in 2016 being 717, according to Traffic Safety Basic Facts 2018. As a comparison, in the European Union, 5320 pedestrians were killed in road accidents, which is 21% of all road fatalities. During the decade 2007-2016, in the European Union, pedestrian fatalities were reduced by 36%, while the total number of fatalities was reduced by almost 41%. The most vulnerable age categories are 0-14 years old and 64+, and the most common causes of road accidents involving pedestrians are: (1) faulty diagnosis - Information failure (between driver and traffic environment or driver and vehicle), (2) observation missed - inadequate plan, (3) observation missed - distraction, (4) observation missed - temporary obstruction to view, (5) Inadequate plan - Psychological stress, (6) Inadequate plan - Insufficient knowledge. Between May 27 and June 11, 2019, a pedestrian trafficking survey questionnaire was applied on a sample of 795 pupils of grades 4 to 8 from 7 elementary schools in Medgidia town of Constanta County, Romania (European Union). The questionnaire contains a set of 15 questions with 3 variants of answer. The objectives of the study were: analyzing pupils' knowledge of road signs and about preventive attitudes in traffic as pedestrians

Possessio. Un viaggio storico-lessicale nel mondo della proprietà terriera medievale della Transilvania

These study has two distinguished parts : a first one, on theoretical bases, regarding the fundamental concepts of glosses and glossaries serving to define the structure of our Glossarium Mediae Latinitatis Actorum Historiam Transsylvaniae, Moldaviae et Transalpinae illustrantium edited by the Editing House of the Romanian Academy in 2010 ; respectively, a second part, focusing on the practice of this Glossarium by means of the technical voice possessio, with the significance of a piece land juridically ascribed to a lord of land (dominus terrestris). In this respect, our purpose was to reconstruct the map of the possessiones in the historical Transsylvania consisting of the seven counties (comitatus) of the Erdély the three counties (comitatus) of The Mark of Temesvár, the counties of the so-called partes ei adnexae, the Sedes Siculicales and the Sedes Saxonicales, also trying to establish the very types and forms of these possessiones

Mastery Guided Non-parametric Clustering to Scale-up Strategy Prediction

Predicting the strategy (sequence of concepts) that a student is likely to use in problem-solving helps Adaptive Instructional Systems (AISs) better adapt themselves to different types of learners based on their learning abilities. This can lead to a more dynamic, engaging, and personalized experience for students. To scale up training a prediction model (such as LSTMs) over large-scale education datasets, we develop a non-parametric approach to cluster symmetric instances in the data. Specifically, we learn a representation based on Node2Vec that encodes symmetries over mastery or skill level since, to solve a problem, it is natural that a student's strategy is likely to involve concepts in which they have gained mastery. Using this representation, we use DP-Means to group symmetric instances through a coarse-to-fine refinement of the clusters. We apply our model to learn strategies for Math learning from large-scale datasets from MATHia, a leading AIS for middle-school math learning. Our results illustrate that our approach can consistently achieve high accuracy using a small sample that is representative of the full dataset. Further, we show that this approach helps us learn strategies with high accuracy for students at different skill levels, i.e., leveraging symmetries improves fairness in the prediction model.Comment: Proceedings of 37th AAAI Conference on Artificial Intelligence Artificial Intelligence for Education. arXiv admin note: substantial text overlap with arXiv:2308.0389

Scalable and Equitable Math Problem Solving Strategy Prediction in Big Educational Data

Understanding a student's problem-solving strategy can have a significant impact on effective math learning using Intelligent Tutoring Systems (ITSs) and Adaptive Instructional Systems (AISs). For instance, the ITS/AIS can better personalize itself to correct specific misconceptions that are indicated by incorrect strategies, specific problems can be designed to improve strategies and frustration can be minimized by adapting to a student's natural way of thinking rather than trying to fit a standard strategy for all. While it may be possible for human experts to identify strategies manually in classroom settings with sufficient student interaction, it is not possible to scale this up to big data. Therefore, we leverage advances in Machine Learning and AI methods to perform scalable strategy prediction that is also fair to students at all skill levels. Specifically, we develop an embedding called MVec where we learn a representation based on the mastery of students. We then cluster these embeddings with a non-parametric clustering method where we progressively learn clusters such that we group together instances that have approximately symmetrical strategies. The strategy prediction model is trained on instances sampled from these clusters. This ensures that we train the model over diverse strategies and also that strategies from a particular group do not bias the DNN model, thus allowing it to optimize its parameters over all groups. Using real world large-scale student interaction datasets from MATHia, we implement our approach using transformers and Node2Vec for learning the mastery embeddings and LSTMs for predicting strategies. We show that our approach can scale up to achieve high accuracy by training on a small sample of a large dataset and also has predictive equality, i.e., it can predict strategies equally well for learners at diverse skill levels.Comment: 12 pages, 7 figures Published as a full paper in the 16th International Conference on Educational Data Mining 202

G-Asks: An Intelligent Automatic Question Generation System for Academic Writing Support

Many electronic feedback systems have been proposed for writing support. However, most of these systems only aim at supporting writing to communicate instead of writing to learn, as in the case of literature review writing. Trigger questions are potentially forms of support for writing to learn, but current automatic question generation approaches focus on factual question generation for reading comprehension or vocabulary assessment. This article presents a novel Automatic Question Generation (AQG) system, called G-Asks, which generates specific trigger questions as a form of support for students' learning through writing. We conducted a large-scale case study, including 24 human supervisors and 33 research students, in an Engineering Research Method course at The University of Sydney and compared questions generated by G-Asks with human generated question. The results indicate that G-Asks can generate questions as useful as human supervisors (`useful' is one of five question quality measures) while significantly outperforming Human Peer and Generic Questions in most quality measures after filtering out questions with grammatical and semantic errors. Furthermore, we identified the most frequent question types, derived from the human supervisors' questions and discussed how the human supervisors generate such questions from the source text

Hybrid analysis of memory references and its application to automatic parallelization

Executing sequential code in parallel on a multithreaded machine has been an elusive goal of the academic and industrial research communities for many years. It has recently become more important due to the widespread introduction of multicores in PCs. Automatic multithreading has not been achieved because classic, static compiler analysis was not powerful enough and program behavior was found to be, in many cases, input dependent. Speculative thread level parallelization was a welcome avenue for advancing parallelization coverage but its performance was not always optimal due to the sometimes unnecessary overhead of checking every dynamic memory reference. In this dissertation we introduce a novel analysis technique, Hybrid Analysis, which unifies static and dynamic memory reference techniques into a seamless compiler framework which extracts almost maximum available parallelism from scientific codes and incurs close to the minimum necessary run time overhead. We present how to extract maximum information from the quantities that could not be sufficiently analyzed through static compiler methods, and how to generate sufficient conditions which, when evaluated dynamically, can validate optimizations. Our techniques have been fully implemented in the Polaris compiler and resulted in whole program speedups on a large number of industry standard benchmark applications

Automated Assessment of Students' Code Comprehension using LLMs

Assessing student's answers and in particular natural language answers is a crucial challenge in the field of education. Advances in machine learning, including transformer-based models such as Large Language Models(LLMs), have led to significant progress in various natural language tasks. Nevertheless, amidst the growing trend of evaluating LLMs across diverse tasks, evaluating LLMs in the realm of automated answer assesment has not received much attention. To address this gap, we explore the potential of using LLMs for automated assessment of student's short and open-ended answer. Particularly, we use LLMs to compare students' explanations with expert explanations in the context of line-by-line explanations of computer programs. For comparison purposes, we assess both Large Language Models (LLMs) and encoder-based Semantic Textual Similarity (STS) models in the context of assessing the correctness of students' explanation of computer code. Our findings indicate that LLMs, when prompted in few-shot and chain-of-thought setting perform comparable to fine-tuned encoder-based models in evaluating students' short answers in programming domain

The Behavior of Large Language Models When Prompted to Generate Code Explanations

This paper systematically investigates the generation of code explanations by Large Language Models (LLMs) for code examples commonly encountered in introductory programming courses. Our findings reveal significant variations in the nature of code explanations produced by LLMs, influenced by factors such as the wording of the prompt, the specific code examples under consideration, the programming language involved, the temperature parameter, and the version of the LLM. However, a consistent pattern emerges for Java and Python, where explanations exhibit a Flesch-Kincaid readability level of approximately 7-8 grade and a consistent lexical density, indicating the proportion of meaningful words relative to the total explanation size. Additionally, the generated explanations consistently achieve high scores for correctness, but lower scores on three other metrics: completeness, conciseness, and specificity

Assessing the intralobular ducts in mouse mandibular gland

In order to quantify the types of intralobular ducts, we used five adult male mice, sacrificed by cervical dislocation, after exposure to anesthetic. After euthanasing the animals, the mandibular glands were histologically processed. We sectioned the tissue at 5 µm thickness and stained the slides using Goldner’s trichrome staining procedure. We captured images on four different microscopic fields for each animal, subsequently counting each type of intralobulary duct. The granular ducts were the most numerous, followed by the intermediary ones and striated ducts with the lowest number