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2022 ◽  
Vol 22 (2) ◽  
pp. 1-27
Author(s):  
Rina P. Y. Lai

Computational Thinking (CT ), entailing both domain-general and domain-specific skills, is a competency fundamental to computing education and beyond. However, as a cross-domain competency, appropriate assessment design and method remain equivocal. Indeed, the majority of the existing assessments have a predominant focus on measuring programming proficiency and neglecting other contexts in which CT can also be manifested. To broaden the promotion and practice of CT, it is necessary to integrate diverse problem types and item formats using a competency-based assessment method to measure CT. Taking a psychometric approach, this article evaluates a novel computer-based assessment of CT competency, Computational Thinking Challenge. The assessment was administered to 119 British upper secondary school students ( M = 16.11; SD = 1.19) with a range of prior programming experiences. Results from several reliability analyses, a convergent validity analysis, and a Rasch analysis, provided evidence to support the quality of the assessment. Taken together, the study demonstrated the feasibility to expand from traditional assessment methods to integrating multiple contexts, problem types, and item formats in measuring CT competency in a comprehensive manner.


2021 ◽  
Author(s):  
Yanming Yang ◽  
Xin Xia ◽  
David Lo ◽  
John Grundy

In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)” and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstrated the powerful learning ability of deep learning and its enormous potential. Deep learning has been increasingly used to develop state-of-the-art software engineering (SE) research tools due to its ability to boost performance for various SE tasks. There are many factors, e.g., deep learning model selection, internal structure differences, and model optimization techniques, that may have an impact on the performance of DNNs applied in SE. Few works to date focus on summarizing, classifying, and analyzing the application of deep learning techniques in SE. To fill this gap, we performed a survey to analyze the relevant studies published since 2006. We first provide an example to illustrate how deep learning techniques are used in SE. We then conduct a background analysis (BA) of primary studies and present four research questions to describe the trend of DNNs used in SE (BA), summarize and classify different deep learning techniques (RQ1), analyze the data processing including data collection, data classification, data pre-processing, and data representation (RQ2). In RQ3, we depicted a range of key research topics using DNNs and investigated the relationships between DL-based model adoption and multiple factors (i.e., DL architectures, task types, problem types, and data types). We also summarized commonly used datasets for different SE tasks. In RQ4, we summarized the widely used optimization algorithms and provided important evaluation metrics for different problem types, including regression, classification, recommendation, and generation. Based on our findings, we present a set of current challenges remaining to be investigated and outline a proposed research road map highlighting key opportunities for future work.


Author(s):  
Benjamin Reuter ◽  
Léo Viallon-Galinier ◽  
Simon Horton ◽  
Alec van Herwijnen ◽  
Stephanie Mayer ◽  
...  
Keyword(s):  

Author(s):  
Michael A. Lones

AbstractThis work uses genetic programming to explore the space of continuous optimisers, with the goal of discovering novel ways of doing optimisation. In order to keep the search space broad, the optimisers are evolved from scratch using Push, a Turing-complete, general-purpose, language. The resulting optimisers are found to be diverse, and explore their optimisation landscapes using a variety of interesting, and sometimes unusual, strategies. Significantly, when applied to problems that were not seen during training, many of the evolved optimisers generalise well, and often outperform existing optimisers. This supports the idea that novel and effective forms of optimisation can be discovered in an automated manner. This paper also shows that pools of evolved optimisers can be hybridised to further increase their generality, leading to optimisers that perform robustly over a broad variety of problem types and sizes.


10.6036/9997 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 546-552
Author(s):  
NAGA-SAI-RAM GOPISETTI ◽  
MARIA LEONILDE ROCHA VARELA ◽  
JOSE MACHADO

Human cognition based procedures are promising approaches for solving different kind of problems, and this paper addresses the part family formation problem inspired by a human cognition procedure through a graph-based approach, drawing on pattern recognition. There are many algorithms which consider nature inspired models for solving a broad range of problem types. However, there is a noticeable existence of a gap in implementing models based on human cognition, which are generally characterized by “visual thinking”, rather than complex mathematical models. Hence, the natural power of reasoning - by detecting the patterns that mimic the natural human cognition - is used in this study as this paper is based on the partial implementation of graph theory in modelling and solving issues related to part machine grouping, regardless of their size. The obtained results have shown that most of the problems solved by using the proposed approach have provided interesting benchmark results when compared with previous results given by GRASP (Greedy Randomized Adaptive Search Procedure) heuristics. Keywords: Cellular manufacturing systems; part family formation; human cognition; inspection-based clustering.


2021 ◽  
Vol 1 ◽  
pp. 3091-3100
Author(s):  
Nicklas Werge Svendsen ◽  
Torben Anker Lenau ◽  
Claus Thorp Hansen

AbstractResearch in biologically-inspired design (BID) practice often focus on team composition or ideation based on an already discovered fascinating biological solution principle. However, how are the outcome of the early design phases affecting BID projects' quality?In this study, historical data from 91 reports from student teams documenting their BID efforts from a 3-week course constitute the data source. Thus, the relationship between design problem types, function types, functions descriptions and BID projects' quality is addressed.The study show that especially design problem types and function descriptions affect the BID projects' quality. For instance, BID projects dealing with open-ended problems yield better results than redesign problems with existing solutions operating in a very domain-limited solution space. Next, BID projects obtain the best results when using functions as drivers for analogy searching rather than properties. Finally, BID projects with certain function types seem to have more complicated conceptualization phases.


2021 ◽  
Vol 11 (11) ◽  
pp. 4774
Author(s):  
Illya Bakurov ◽  
Marco Buzzelli ◽  
Mauro Castelli ◽  
Leonardo Vanneschi ◽  
Raimondo Schettini

Several interesting libraries for optimization have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. In this paper, we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimization and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction).


2021 ◽  
Author(s):  
Clemens Brunner ◽  
Nikolaus A Koren ◽  
Judith Scheucher ◽  
Jochen A. Mosbacher ◽  
Bert De Smedt ◽  
...  

Numerous studies have identified neurophysiological correlates of performing arithmetic in adults. For example, oscillatory electroencephalographic (EEG) patterns associated with retrieval and procedural strategies are well established. Whereas fact retrieval has been linked to enhanced left-hemispheric theta ERS (event-related synchronization), procedural strategies are accompanied by increased bilateral alpha ERD (event-related desynchronization). It is currently not clear if these findings generalize to children.Our study is the first to investigate oscillatory EEG activity related to strategy use and arithmetic operations in children. We assessed ERD/ERS correlates of 31 children in fourth grade (aged between nine and ten years) during arithmetic problem solving. We presented multiplication and subtraction problems, which children solved with fact retrieval or via a procedure. Based on both problem size and verbal strategy reports, we analyzed these problem types separately for each operation.We found similar strategy-related patterns to those reported in previous studies with adults. That is, retrieval problems elicited stronger left-hemispheric theta ERS and weaker alpha ERD as compared to procedural problems. Interestingly, we observed differences between multiplications and subtractions within retrieval problems. Although there were no response time and accuracy differences, retrieved multiplications were accompanied by larger theta ERS than retrieved subtractions. This finding could indicate that retrieval of multiplication and subtraction facts are distinct processes, and/or that multiplications are more frequently retrieved than subtractions in this age group.


Author(s):  
Jack Rueter ◽  
Mika Hämäläinen

This paper presents the current lexical, morphological, syntactic and rule-based machine translation work for Erzya and Moksha that can and should be used in the development of a roadmap for Mordvin linguistic research. We seek to illustrate and outline initial problem types to be encountered in the construction of an Apertium-based shallow-transfer machine translation system for the Mordvin language forms. We indicate reference points within Mordvin Studies and other parts of Uralic studies, as a point of departure for outlining a linguistic studies with a means for measuring its own progress and developing a roadmap for further studies.


2021 ◽  
Vol 7 (1) ◽  
pp. 66-81
Author(s):  
Madhur Sharma ◽  
Satwat Bashir ◽  
Gaurav Suri

Single-digit, three addend sums of the type a + b + c offer a rich opportunity to directly observe the range of strategies that different participants may use because they afford the possibility of measuring a partial sum (i.e., a + b or a + c or b + c). For example, while computing the sum 9 + 7 + 1, do participants go in order by first adding 9 + 7 and then adding 1, or do they incur the cost of going out of order by adding 9 + 1 in order to obtain the partial sum of 10, which makes the subsequent addition of 7 less effortful? Informed by findings in simple and complex arithmetic, we investigated the problem types and participant characteristics that can predict out of order switching behavior in such three-addend sums. To test our hypotheses, we tasked participants, first in an online study, and then in an in-person study to complete 120 single-digit, three addend problems. We found that participants switched the order of addition to prioritize efficiency gains in contexts in which the partial sum addends were small or equal to each other, or when doing so led to a partial sum of 10, or led to a partial sum that is equal to the third remaining integer. Response latency data confirmed that participants were deriving efficiencies in the manner we expected. Related to individual differences, our findings showed that participants with higher levels of math education were most likely to seek efficiency benefits whenever they were on offer.


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