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2022 ◽  
Vol 22 (1) ◽  
pp. 1-46
Author(s):  
Sarah Heckman ◽  
Jeffrey C. Carver ◽  
Mark Sherriff ◽  
Ahmed Al-zubidy

Context. Computing Education Research (CER) is critical to help the computing education community and policy makers support the increasing population of students who need to learn computing skills for future careers. For a community to systematically advance knowledge about a topic, the members must be able to understand published work thoroughly enough to perform replications, conduct meta-analyses, and build theories. There is a need to understand whether published research allows the CER community to systematically advance knowledge and build theories. Objectives. The goal of this study is to characterize the reporting of empiricism in Computing Education Research literature by identifying whether publications include content necessary for researchers to perform replications, meta-analyses, and theory building. We answer three research questions related to this goal: (RQ1) What percentage of papers in CER venues have some form of empirical evaluation? (RQ2) Of the papers that have empirical evaluation, what are the characteristics of the empirical evaluation? (RQ3) Of the papers that have empirical evaluation, do they follow norms (both for inclusion and for labeling of information needed for replication, meta-analysis, and, eventually, theory-building) for reporting empirical work? Methods. We conducted a systematic literature review of the 2014 and 2015 proceedings or issues of five CER venues: Technical Symposium on Computer Science Education (SIGCSE TS), International Symposium on Computing Education Research (ICER), Conference on Innovation and Technology in Computer Science Education (ITiCSE), ACM Transactions on Computing Education (TOCE), and Computer Science Education (CSE). We developed and applied the CER Empiricism Assessment Rubric to the 427 papers accepted and published at these venues over 2014 and 2015. Two people evaluated each paper using the Base Rubric for characterizing the paper. An individual person applied the other rubrics to characterize the norms of reporting, as appropriate for the paper type. Any discrepancies or questions were discussed between multiple reviewers to resolve. Results. We found that over 80% of papers accepted across all five venues had some form of empirical evaluation. Quantitative evaluation methods were the most frequently reported. Papers most frequently reported results on interventions around pedagogical techniques, curriculum, community, or tools. There was a split in papers that had some type of comparison between an intervention and some other dataset or baseline. Most papers reported related work, following the expectations for doing so in the SIGCSE and CER community. However, many papers were lacking properly reported research objectives, goals, research questions, or hypotheses; description of participants; study design; data collection; and threats to validity. These results align with prior surveys of the CER literature. Conclusions. CER authors are contributing empirical results to the literature; however, not all norms for reporting are met. We encourage authors to provide clear, labeled details about their work so readers can use the study methodologies and results for replications and meta-analyses. As our community grows, our reporting of CER should mature to help establish computing education theory to support the next generation of computing learners.


2022 ◽  
Vol 114 ◽  
pp. 105962
Author(s):  
Julie Ingram ◽  
Damian Maye ◽  
Clive Bailye ◽  
Andrew Barnes ◽  
Christopher Bear ◽  
...  

2022 ◽  
Vol 40 (4) ◽  
pp. 1-35
Author(s):  
Tetsuya Sakai ◽  
Sijie Tao ◽  
Zhaohao Zeng

In the context of depth- k pooling for constructing web search test collections, we compare two approaches to ordering pooled documents for relevance assessors: The prioritisation strategy (PRI) used widely at NTCIR, and the simple randomisation strategy (RND). In order to address research questions regarding PRI and RND, we have constructed and released the WWW3E8 dataset, which contains eight independent relevance labels for 32,375 topic-document pairs, i.e., a total of 259,000 labels. Four of the eight relevance labels were obtained from PRI-based pools; the other four were obtained from RND-based pools. Using WWW3E8, we compare PRI and RND in terms of inter-assessor agreement, system ranking agreement, and robustness to new systems that did not contribute to the pools. We also utilise an assessor activity log we obtained as a byproduct of WWW3E8 to compare the two strategies in terms of assessment efficiency. Our main findings are: (a) The presentation order has no substantial impact on assessment efficiency; (b) While the presentation order substantially affects which documents are judged (highly) relevant, the difference between the inter-assessor agreement under the PRI condition and that under the RND condition is of no practical significance; (c) Different system rankings under the PRI condition are substantially more similar to one another than those under the RND condition; and (d) PRI-based relevance assessment files (qrels) are substantially and statistically significantly more robust to new systems than RND-based ones. Finding (d) suggests that PRI helps the assessors identify relevant documents that affect the evaluation of many existing systems, including those that did not contribute to the pools. Hence, if researchers need to evaluate their current IR systems using legacy IR test collections, we recommend the use of those constructed using the PRI approach unless they have a good reason to believe that their systems retrieve relevant documents that are vastly different from the pooled documents. While this robustness of PRI may also mean that the PRI-based pools are biased against future systems that retrieve highly novel relevant documents, one should note that there is no evidence that RND is any better in this respect.


Author(s):  
I Made Agus Wirawan ◽  
Retantyo Wardoyo ◽  
Danang Lelono

Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the EEG signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: i) What factors need to be considered to generate and distribute EEG data?, ii) How can EEG signals be generated with consideration of differences in participant characteristics?, and iii) How do EEG signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in EEG signals-based emotion recognition research. These include i) determine robust methods for imbalanced EEG signals data, ii) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, iii) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the EEG signals, iv) determine the robust architecture of the capsule network method to overcome the loss of knowledge information and apply it in more diverse data set.


Author(s):  
أحمد ماهر خفاجة شحاتة

Despite the availability of millions of information resources on the internet, the Arabic digital content represents a relatively small percentage compared with the information available in other languages. The size of Arabic content, the lack of an adequate number of Arabic databases that organize this content and make it available to the Arab reader, and the lack of novelty and originality are the main issues that feature the Arabic content on the internet. The aim of the current study is to clarify the Arab scholars’ perception regarding the quality, reliability, and suitability of Arabic digital content that is available on the internet. A quantitative approach was adopted in this study in order to answer the research questions. A questionnaire was distributed online among a sample of Arab scholars to determine the quality and reliability of the Arabic digital content. Moreover, the questionnaire tried to identify the extent to which the current Arabic digital content meets the growing information needs, to identify the Arab scholars’ uses of Arabic content, and to discover the criteria that determine the digital content suitability. The findings of this study revealed that Arab scholars believe that Arabic digital content is weak and there is a lack of originality. In addition, the results indicated that Arabic digital content on the internet does not satisfy the scholars' needs which enforce them to use English information resources to compensates for the lack of Arabic resources. The study recommended the necessity of establishing mechanisms to support Arabic digital content and increase the academic institutions' role in enhancing Arabic digital content by encouraging and supporting scholarly research in the Arabic language.


Author(s):  
Juan Ramón Jiménez-García ◽  
Antonina Levatino

AbstractThis article examines the socio-occupational integration of the immigrant population in Spain for a time span that, for the first time, includes the post-crisis period. Using the Spanish Labour Force Survey and conducting a socio-occupational analysis, we predict the probability that a migrant would be employed in one socio-occupational class over another in three periods: before, during and after the crisis. Our main research questions are as follows: (1) To what extent do migrants tend to be located in certain socio-occupational classes? (2) To what extent does the likelihood of belonging to a certain socio-occupational class differ according to immigrants’ places of origin? (3) Can differences be found in the likelihood of belonging to a certain socio-occupational class according to the places of origin before, during and after the Great Recession? The results show a very unequal distribution of immigrants in the socio-occupational structure according to their origin. While immigrants from Schengen Europe and North America are better located in the occupational structure, those from Eastern Europe and Africa are over-represented in the lower socio-occupational classes.


2022 ◽  
Author(s):  
Pablo Sánchez ◽  
Alejandro Bellogín

Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks (LBSNs) are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done in the last 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also alert about the lack of reproducibility in the field that may hinder real performance improvements.


2022 ◽  
Author(s):  
Paula Delgado-Santos ◽  
Giuseppe Stragapede ◽  
Ruben Tolosana ◽  
Richard Guest ◽  
Farzin Deravi ◽  
...  

The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions are presented for further advancements in the field.


Author(s):  
Richard Garfinkle ◽  
Rebecca P. Petersen ◽  
Chris DuCoin ◽  
Maria S. Altieri ◽  
Rajesh Aggarwal ◽  
...  

2022 ◽  
Vol 12 (1) ◽  
pp. 43
Author(s):  
Sandra A. Lampley ◽  
Sarah Roller Dyess ◽  
Michael P. J. Benfield ◽  
Andrew M. Davis ◽  
Sampson E. Gholston ◽  
...  

There is a demand for more STEM professionals. Early elementary students’ conceptions about engineering can influence whether or not they explore STEM career paths and ultimately select an engineering career. This study examined the conceptions elementary students have regarding the work that engineers perform. The research questions were the following: (1) what images do early elementary students associate with engineering and engineers, (2) do these associations vary from grade to grade, (3) are there gendered differences in these associations, and (4) how do the associations from this sample compare with the associations from the broader (grades one–five) Cunningham, Lachapelle, and Lindgren-Steider (2005) sample? Survey data from 1811 students in grades one–three were analyzed by comparison analysis and cluster analysis and then compared to the initial Cunningham et al. (2005) study. The results indicate two ways elementary students envision engineering: (a) creating designs or collecting and analyzing data, and (b) utilizing equipment to build and improve things. Comparison with the Cunningham et al. (2005) study suggests that there may be shifts in the way elementary students perceive engineering. Since these shifts could be attributed to a variety of factors, future work that determines what learning experiences might be contributing to students’ conceptions about engineering is recommended.


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