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1. Empirical Re-Conceptualization as a Bridge to Insight

   Ellis, A.B. (July 2020, The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS))

   Identifying patterns is an important part of mathematical investigation, but many students struggle to justify their pattern-based generalizations. These findings have led some to argue for a de-emphasis on patterning, but others argue that it can support insight into a problem’s structure. We introduce a phenomenon, empirical re-conceptualization, in which learners generalize based on an empirical pattern, and then re-interpret it from a structural perspective. We elaborate this construct by providing a representative example of empirical reconceptualization from two secondary students. Our findings indicate that developing empirical results can foster subsequent insights, which can in turn lead to justification and proof. 

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2. A Case of a Computational Setting Facilitating Empirical Re-Conceptualization

   De_Chenne, Adaline; Lockwood, Elise (February 2023, RUME XXV Conference Proceedings)

   The use of computer programming is ubiquitous for research mathematicians, and two common practices are facilitation of experimentation and testing of conjectures. These practices align with empirical re-conceptualization, which is the process where empirical data is used to identify patterns, form related conjectures, and then re-interpret the conjectures from a structural perspective. However, literature on empirical re-conceptualization has only examined student work done by hand. We present case study data of one student, Allen, using the programming language Python to facilitate empirical re-conceptualization as he found the closed-form solution for binomial coefficient C(n,k). We discuss why the computational environment was productive for empirical re-conceptualization and provide avenues for future research. 

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3. Towards reproducible research: automatic classification of empirical requirements engineering papers

   https://doi.org/10.1145/3190645.3190689  

   Woodson, Clinton; Hayes, Jane Huffman; Griffioen, Sarah (January 2018, ACMSE '18 Proceedings of the ACMSE 2018 Conference)

   Research must be reproducible in order to make an impact on science and to contribute to the body of knowledge in our field. Yet studies have shown that 70% of research from academic labs cannot be reproduced. In software engineering, and more specifically requirements engineering (RE), reproducible research is rare, with datasets not always available or methods not fully described. This lack of reproducible research hinders progress, with researchers having to replicate an experiment from scratch. A researcher starting out in RE has to sift through conference papers, finding ones that are empirical, then must look through the data available from the empirical paper (if any) to make a preliminary determination if the paper can be reproduced. This paper addresses two parts of that problem, identifying RE papers and identifying empirical papers within the RE papers. Recent RE and empirical conference papers were used to learn features and to build an automatic classifier to identify RE and empirical papers. We introduce the Empirical Requirements Research Classifier (ERRC) method, which uses natural language processing and machine learning to perform supervised classification of conference papers. We compare our method to a baseline keyword-based approach. To evaluate our approach, we examine sets of papers from the IEEE Requirements Engineering conference and the IEEE International Symposium on Software Testing and Analysis. We found that the ERRC method performed better than the baseline method in all but a few cases. 

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4. Teaching requirements elicitation interviews: an empirical study of learning from mistakes

   https://doi.org/10.1007/s00766-019-00313-0  

   Bano, Muneera; Zowghi, Didar; Ferrari, Alessio; Spoletini, Paola; Donati, Beatrice (January 2019, Requirements Engineering)

   Interviews are the most widely used elicitation technique in requirements engineering (RE). However, conducting a requirements elicitation interview is challenging. The mistakes made in design or conduct of the interviews can create problems in the later stages of requirements analysis. Empirical evidence about effective pedagogical approaches for training novices on conducting requirements elicitation interviews is scarce. In this paper, we present a novel pedagogical approach for training student analysts in the art of elicitation interviews. Our study is conducted in two parts: first, we perform an observational study of interviews performed by novices, and we present a classification of the most common mistakes made; second, we utilize this list of mistakes and monitor the students’ progress in three set of interviews to discover the individual areas for improvement. We conducted an empirical study involving role-playing and authentic assessment in two semesters on two different cohorts of students. In the first semester, we had 110 students, teamed up in 28 groups, to conduct three interviews with stakeholders. We qualitatively analysed the data to identify and classify the mistakes made from their first interview only. In the second semester, we had 138 students in 34 groups and we monitored and analysed their progress in all three interviews by utilizing the list of mistakes from the first study. First, we identified 34 unique mistakes classified into seven high-level themes, namely question formulation, question omission, interview order, communication skills, analyst behaviour, customer interaction, teamwork and planning. In the second study, we discovered that the students struggled mostly in the areas of question formulation, question omission and interview order and did not manage to improve their skills throughout the three interviews. Our study presents a novel and repeatable pedagogical design, and our findings extend the body of knowledge aimed at RE education and training by providing an empirically grounded categorization of mistakes made by novices. We offer an analysis of the main pain points in which instructors should pay more attention during their design and training. 

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5. Biculturalism and Adjustment Among U.S. Latinos: A Review of Four Decades of Empirical Findings

   https://doi.org/10.1016/bs.acdb.2021.05.002  

   Safa, M. D.; Umaña-Taylor, A. J. (January 2021, Advances in child development and behavior)

   Incorporating both heritage (e.g., Latino) and national (e.g., American) cultural systems into our sense of self (i.e., bicultural identity) and developing the ability to successfully respond to demands associated with each of these systems (i.e., bicultural competence) have been theorized to be central to the development and positive adjustment of U.S. Latinos, a group that represents a large segment of the country’s population. In this comprehensive review, we examined empirical research on biculturalism among U.S. Latinos spanning four decades (1980-2020), with a focus on synthesizing the field’s understanding of antecedents and consequents associated with biculturalism and identifying directions for future research. Our review of 152 empirical articles revealed that this literature was characterized by multiple approaches to the conceptualization (i.e., dual-cultural adaptation, dual-cultural identities, bicultural identity integration, and bicultural competence) and subsequent operationalization of biculturalism. Although each conceptualization has different implications for the conclusions that can be drawn regarding an aspect or facet of biculturalism and potential influence on adjustment, a significant majority (78%) of studies, across conceptualizations, provided evidence supporting a positive association between biculturalism and Latinos’ adjustment. In addition, a relatively small body of qualitative work was identified, and findings largely informed potential antecedents of biculturalism. We review this literature with attention to: the aspects of biculturalism that are associated with Latinos’ adjustment (i.e., psychosocial and physical health), how sample characteristics may limit the generalizability of this work, and important directions for future research both in terms of conceptualization and study design. 

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