Undergraduates lived experience of project-/problem-based learning in Introductory Biology

Recommendations for enhancing scientific literacy, inclusivity, and the ecosystem for innovation call for transitioning from teacher-centered to learner-centered science classrooms, particularly at the introductory undergraduate level. Yet, little is documented about the challenges that undergraduates perceive in such classrooms and on students' ways of navigating them. Via mixed methods, we studied undergraduates' lived experience in one form of learner-centered teaching, hybrid project-/problem-based learning (PBL), in introductory organismal biology at a baccalaureate institution. Prominent in qualitative analyses of student interviews and written reflections were undergraduates' initial expectation of and longing for an emphasis on facts and transmission of them. The prominence diminished from semester's middle to end, as students came to value developing ideas, solving problems collaboratively, and engaging in deep ways of learning. Collaboration and personal resources such as belief in self emerged as supports for these shifts. Quantitative analyses corroborated that PBL students transformed as learners, moving toward informed views on nature of science, advancing in multi-variable causal reasoning, and more frequently adopting deep approaches for learning than did students in lecture-based sections. The qualitative and quantitative findings portray the PBL classroom as an intercultural experience in which culture shock yields over time to acceptance in a way supported by students' internal resources and peer collaboration. The findings have value to those seeking to implement PBL and other complex-learning approaches in a manner responsive to the lived experience of the learner.

Decision prioritization and causal reasoning in decision hierarchies

From cooking a meal to finding a route to a destination, many real life decisions can be decomposed into a hierarchy of sub-decisions. In a hierarchy, choosing which decision to think about requires planning over a potentially vast space of possible decision sequences. To gain insight into how people decide what to decide on, we studied a novel task that combines perceptual decision making, active sensing and hierarchical and counterfactual reasoning. Human participants had to find a target hidden at the lowest level of a decision tree. They could solicit information from the different nodes of the decision tree to gather noisy evidence about the target’s location. Feedback was given only after errors at the leaf nodes and provided ambiguous evidence about the cause of the error. Despite the complexity of task (with 107 latent states) participants were able to plan efficiently in the task. A computational model of this process identified a small number of heuristics of low computational complexity that accounted for human behavior. These heuristics include making categorical decisions at the branching points of the decision tree rather than carrying forward entire probability distributions, discarding sensory evidence deemed unreliable to make a choice, and using choice confidence to infer the cause of the error after an initial plan failed. Plans based on probabilistic inference or myopic sampling norms could not capture participants’ behavior. Our results show that it is possible to identify hallmarks of heuristic planning with sensing in human behavior and that the use of tasks of intermediate complexity helps identify the rules underlying human ability to reason over decision hierarchies.

A Functional Network Model of the Metastasis Suppressor PEBP1/RKIP and Its Regulators in Breast Cancer Cells

Drug screening strategies focus on quantifying the phenotypic effects of different compounds on biological systems. High-throughput technologies have the potential to understand further the mechanisms by which these drugs produce the desired outcome. Reverse causal reasoning integrates existing biological knowledge and measurements of gene and protein abundances to infer their function. This approach can be employed to appraise the existing biological knowledge and data to prioritize targets for cancer therapies. We applied text mining and a manual literature search to extract known interactions between several metastasis suppressors and their regulators. We then identified the relevant interactions in the breast cancer cell line MCF7 using a knockdown dataset. We finally adopted a reverse causal reasoning approach to evaluate and prioritize pathways that are most consistent and responsive to drugs that inhibit cell growth. We evaluated this model in terms of agreement with the observations under treatment of several drugs that produced growth inhibition of cancer cell lines. In particular, we suggested that the metastasis suppressor PEBP1/RKIP is on the receiving end of two significant regulatory mechanisms. One involves RELA (transcription factor p65) and SNAI1, which were previously reported to inhibit PEBP1. The other involves the estrogen receptor (ESR1), which induces PEBP1 through the kinase NME1. Our model was derived in the specific context of breast cancer, but the observed responses to drug treatments were consistent in other cell lines. We further validated some of the predicted regulatory links in the breast cancer cell line MCF7 experimentally and highlighted the points of uncertainty in our model. To summarize, our model was consistent with the observed changes in activity with drug perturbations. In particular, two pathways, including PEBP1, were highly responsive and would be likely targets for intervention.

Causal Reasoning Model Based on Medical Knowledge Graph for Disease Diagnosis

In view of the non-interpretability of disease diagnosis models based on deep learning, a knowledge reasoning model based on medical knowledge graph for intelligent diagnosis is proposed. Given the patient symptom set, the co-occurrence of the patient and the disease is calculated, then the patient suffering from one disease is calculated. Based on the dynamic threshold value, the final disease diagnosis result of the patient is outputted. According to the symptoms of patients and the symptoms in the knowledge graph, the causal reasoning of the disease diagnosis is interpretable. Experiments on 145,712 pediatric electronic medical records in Chinese show that the proposed model can predict diseases with interpretability, and the accuracy reaches-82.12%.

A puzzle about narrative progression and causal reasoning

This chapter argues that key to an analysis of narrative progression are aspectual constraints imposed by coherence relations. This argument is based on a discourse like “A cat bit into a mouse while it was wiggling its tail. It was dead”. The fact that it’s infelicitous is remarkable given that the following is fine: “A mouse was dead. A cat bit into it while it was wiggling its tail”. The chapter explains these data in two steps. First, it proposes definitions for the coherence relations, RESULT and EXPLANATION, in which the former, but not the latter, rules out stative arguments. Second, it provides axioms in a default logic which predict the conditions under which these and competing coherence relations are typically inferred. It provides independent evidence for the proposed analysis from discourses involving exclamatives, temporal indexicals, and deverbals. It also considers discourses that challenge the analysis involving perspectival expressions.

Changing Minds ― Epistemic Interventions in Causal Reasoning

Did Tom’s use of nuts in the dish cause Billy’s allergic reaction? According to counterfactual theories of causation, an agent is judged a cause to the extent that their action made a difference to the outcome (Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2020; Gerstenberg, Halpern, & Tenenbaum, 2015; Halpern, 2016; Hitchcock & Knobe, 2009). In this paper, we argue for the integration of epistemic states into current counterfactual accounts of causation. In the case of ignorant causal agents, we demonstrate that people’s counterfactual reasoning primarily targets the agent’s epistemic state – what the agent doesn’t know –, and their epistemic actions – what they could have done to know – rather than the agent’s actual causal action. In four experiments, we show that people’s causal judgment as well as their reasoning about alternatives is sensitive to the epistemic conditions of a causal agent: Knowledge vs. ignorance (Experiment 1), self-caused vs. externally caused ignorance (Experiment 2), the number of epistemic actions (Experiment 3), and the epistemic context (Experiment 4). We see two advantages in integrating epistemic states into causal models and counterfactual frameworks. First, assuming the intervention on indirect, epistemic causes might allow us to explain why people attribute decreased causality to ignorant vs. knowing causal agents. Moreover, causal agents’ epistemic states pick out those factors that can be controlled or manipulated in order to achieve desirable future outcomes, reflecting the forward-looking dimension of causality. We discuss our findings in the broader context of moral and causal cognition.

Supporting Student System Modelling Practice Through Curriculum and Technology Design

Developing and using models to make sense of phenomena or to design solutions to problems is a key science and engineering practice. Classroom use of technology-based tools can promote the development of students’ modelling practice, systems thinking, and causal reasoning by providing opportunities to develop and use models to explore phenomena. In previous work, we presented four aspects of system modelling that emerged during our development and initial testing of an online system modelling tool. In this study, we provide an in-depth examination and detailed evidence of 10th grade students engaging in those four aspects during a classroom enactment of a system modelling unit. We look at the choices students made when constructing their models, whether they described evidence and reasoning for those choices, and whether they described the behavior of their models in connection with model usefulness in explaining and making predictions about the phenomena of interest. We conclude with a set of recommendations for designing curricular materials that leverage digital tools to facilitate the iterative constructing, using, evaluating, and revising of models.