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1. Causal Relational Learning

   https://doi.org/10.1145/3318464.3389759  

   Salimi, Babak; Parikh, Harsh; Kayali, Moe; Getoor, Lise; Roy, Sudeepa; Suciu, Dan (May 2020, SIGMOD)

   Causal inference is at the heart of empirical research in natu- ral and social sciences and is critical for scientific discovery and informed decision making. The gold standard in causal inference is performing randomized controlled trials; unfortu- nately these are not always feasible due to ethical, legal, or cost constraints. As an alternative, methodologies for causal inference from observational data have been developed in sta- tistical studies and social sciences. However, existing meth- ods critically rely on restrictive assumptions such as the study population consisting of homogeneous elements that can be represented in a single flat table, where each row is referred to as a unit. In contrast, in many real-world set- tings, the study domain naturally consists of heterogeneous elements with complex relational structure, where the data is naturally represented in multiple related tables. In this paper, we present a formal framework for causal inference from such relational data. We propose a declarative language called CaRL for capturing causal background knowledge and assumptions, and specifying causal queries using simple Datalog-like rules. CaRL provides a foundation for infer- ring causality and reasoning about the effect of complex interventions in relational domains. We present an extensive experimental evaluation on real relational data to illustrate the applicability of CaRL in social sciences and healthcare. 

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2. An Interdisciplinary Learning Community of Education and Psychology Majors

   Yates, S. (January 2023, Education sciences)

   The researchers conducted a qualitative case study to describe the experiences (over the course of a semester) of an inter-disciplinary team of three special education and three psychology undergraduates who participated in a relational learning community and a graduate student who designed and facilitated this learning community. An associate professor and special education researcher oversaw and co-facilitated the project. The design of the learning community promoted the building of rapport and trust among the group members and the progress of the group toward a common goal of incorporating principles from psychology to develop teaching strategies for students who are struggling in math and experiencing severe math anxiety. Gathering more frequent and individualized feedback would have helped the learning community facilitator make some key adjustments earlier in the project, but the incorporation of rapport building activities that supported trust and collaboration among the group was supportive of group progress toward a common goal. We learned key lessons about how to design and implement a learning community that can be applied to the field of education, interdisciplinary collaboration, and other contexts. 

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3. Capturing Self-Regulated Learning During Search

   Urgo, Kelsey; Arguello, Jaime (July 2022, Workshop on Investigating Learning During Web Search)

   Researchers in the learning sciences have demonstrated the benefits of effective self-regulated learning (SRL) in improving learning outcomes. The search-as-learning community aims to improve learning outcomes during search, but offers limited research exploring the impact of SRL on learning during search. Current limited research in search-as-learning explores only \textit{perceptions} of SRL processes \textit{after} the search process~\cite{crescenzi_supporting_2021}. Results from such analyses are limited in that SRL is a dynamic, active process and participant perceptions of SRL can be unreliable~\cite{winne_exploring_2002, greene_domain-specificity_2015}. In this paper, we propose the implementation of an SRL coding framework to capture SRL processes as they unfold throughout a search session. Additionally, we offer several implications for future work using the proposed methodology. 

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4. The Language Science Station at Planet Word: a language research and engagement laboratory at a language museum

   https://doi.org/10.1515/lingvan-2024-0077  

   Vaughn, Charlotte; Mechtenberg, Hannah; Contreras, Jessica Orozco (September 2024, Linguistics Vanguard)

   The Language Science Station (LSS) is a research and engagement laboratory operating at the Planet Word museum in Washington, DC, representing a unique partnership between language researchers and a museum dedicated to language. The LSS invites Planet Word guests – ranging from local to international visitors – to participate in research studies and engage in educational activities with student language scientists from diverse academic backgrounds. In doing so, we broaden participation in the language sciences among both the researchers and the participant population. This paper outlines the goals, values, and structure of the LSS, highlighting our dual emphases on research and engagement. We focus on several aspects of the project. These include our novel multi-university researcher-museum partnership, the different considerations that we find are necessary for conducting research in a museum setting compared to the laboratory, and our training of researchers and student research assistants. The paper also provides reflections from students on their interactions with museum visitors. We share our experiences with the broader scholarly community in an effort to lower barriers for other behavioral scientists interested in combining research and engagement in public venues. 

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5. Meta Learning with Relational Information for Short Sequences

   Xie, Y; Jiang, H; Liu, F; Zhao, T; Zha, H (January 2019, Advances in neural information processing systems)

   This paper proposes a new meta-learning method – named HARMLESS (HAwkes Relational Meta LEarning method for Short Sequences) for learning heterogeneous point process models from short event sequence data along with a relational network. Specifically, we propose a hierarchical Bayesian mixture Hawkes process model, which naturally incorporates the relational information among sequences into point process modeling. Compared with existing methods, our model can capture the underlying mixed-community patterns of the relational network, which simultaneously encourages knowledge sharing among sequences and facilitates adaptive learning for each individual sequence. We further propose an efficient stochastic variational meta expectation maximization algorithm that can scale to large problems. Numerical experiments on both synthetic and real data show that HARMLESS outperforms existing methods in terms of predicting the future events. 

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