This review summarizes selected studies on galectin-3 (Gal3) as an example of the dynamic behavior of a carbohydrate-binding protein in the cytoplasm and nucleus of cells. Within the 15-member galectin family of proteins, Gal3 (Mr ~30,000) is the sole representative of the chimera subclass in which a proline- and glycine-rich NH2-terminal domain is fused onto a COOH-terminal carbohydrate recognition domain responsible for binding galactose-containing glycoconjugates. The protein shuttles between the cytoplasm and nucleus on the basis of targeting signals that are recognized by importin(s) for nuclear localization and exportin-1 (CRM1) for nuclear export. Depending on the cell type, specific experimental conditions in vitro, or tissue location, Gal3 has been reported to be exclusively cytoplasmic, predominantly nuclear, or distributed between the two compartments. The nuclear versus cytoplasmic distribution of the protein must reflect, then, some balance between nuclear import and export, as well as mechanisms of cytoplasmic anchorage or binding to a nuclear component. Indeed, a number of ligands have been reported for Gal3 in the cytoplasm and in the nucleus. Most of the ligands appear to bind Gal3, however, through protein-protein interactions rather than through protein-carbohydrate recognition. In the cytoplasm, for example, Gal3 interacts with the apoptosis repressor Bcl-2 and this interaction may be involved in Gal3’s anti-apoptotic activity. In the nucleus, Gal3 is a required pre-mRNA splicing factor; the protein is incorporated into spliceosomes via its association with the U1 small nuclear ribonucleoprotein (snRNP) complex. Although the majority of these interactions occur via the carbohydrate recognition domain of Gal3 and saccharide ligands such as lactose can perturb some of these interactions, the significance of the protein’s carbohydrate-binding activity, per se, remains a challenge for future investigations.

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Students’ writing can provide better insight into their thinking than can multiple-choice questions. However, resource constraints often prevent faculty from using writing assessments in large undergraduate science courses. We investigated the use of computer software to analyze student writing and to uncover student ideas about chemistry in an introductory biology course. Students were asked to predict acid–base behavior of biological functional groups and to explain their answers. Student explanations were rated by two independent raters. Responses were also analyzed using SPSS Text Analysis for Surveys and a custom library of science-related terms and lexical categories relevant to the assessment item. These analyses revealed conceptual connections made by students, student difficulties explaining these topics, and the heterogeneity of student ideas. We validated the lexical analysis by correlating student interviews with the lexical analysis. We used discriminant analysis to create classification functions that identified seven key lexical categories that predict expert scoring (interrater reliability with experts = 0.899). This study suggests that computerized lexical analysis may be useful for automatically categorizing large numbers of student open-ended responses. Lexical analysis provides instructors unique insights into student thinking and a whole-class perspective that are difficult to obtain from multiple-choice questions or reading individual responses.

This study develops a framework to conceptualize the use and evolution of machine learning (ML) in science assessment. We systematically reviewed 47 studies that applied ML in science assessment and classified them into five categories: (a) constructed response, (b) essay, (c) simulation, (d) educational game, and (e) inter‐discipline. We compared the ML‐based and conventional science assessments and extracted 12 critical characteristics to map three variables in a three‐dimensional framework: construct, functionality, and automaticity. The 12 characteristics used to construct a profile for ML‐based science assessments for each article were further analyzed by a two‐step cluster analysis. The clusters identified for each variable were summarized into four levels to illustrate the evolution of each. We further conducted cluster analysis to identify four classes of assessment across the three variables. Based on the analysis, we conclude that ML has transformed—but not yet redefined—conventional science assessment practice in terms of fundamental purpose, the nature of the science assessment, and the relevant assessment challenges. Along with the three‐dimensional framework, we propose five anticipated trends for incorporating ML in science assessment practice for future studies: addressing developmental cognition, changing the process of educational decision making, personalized science learning, borrowing 'good' to advance 'good', and integrating knowledge from other disciplines into science assessment.

Recent calls for college biology education reform have identified “pathways and transformations of matter and energy” as a big idea in biology crucial for students to learn. Previous work has been conducted on how college students think about such matter-transforming processes; however, little research has investigated how students connect these ideas. Here, we probe student thinking about matter transformations in the familiar context of human weight loss. Our analysis of 1192 student constructed responses revealed three scientific (which we label “Normative”) and five less scientific (which we label “Developing”) ideas that students use to explain weight loss. Additionally, students combine these ideas in their responses, with an average number of 2.19 ± 1.07 ideas per response, and 74.4% of responses containing two or more ideas. These results highlight the extent to which students hold multiple (both correct and incorrect) ideas about complex biological processes. We described student responses as conforming to either Scientific, Mixed, or Developing descriptive models, which had an average of 1.9 ± 0.6, 3.1 ± 0.9, and 1.7 ± 0.8 ideas per response, respectively. Such heterogeneous student thinking is characteristic of difficulties in both conceptual change and early expertise development and will require careful instructional intervention for lasting learning gains.