Biology education research: building integrative frameworks for teaching and learning about living systems

Nehm, Ross H.

doi:10.1186/s43031-019-0017-6

Cited by 30 publications

(32 citation statements)

References 59 publications

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“…The responses collected in this project are rich in science content, symbols, and relationships among components (e.g., differential movement of two ions), which can be challenges to developing accurate ML models using limited training sets, as is often the case in science assessments. Therefore, correct classification relies on our underlying conceptual framework to guide the ML identification of specific ideas and connections that we were interested in finding (Nehm 2019). The ML analytic approach was able to identify the most important and frequent ideas, as has been found before (Sieke et al 2019;Moharreri et al 2014).…”

Section: Discussionmentioning

confidence: 99%

“…Such holistic coding schemes may be developed based on "correctness" of an explanation; however, not all such schemes may be closely tied to an underlying framework. Such frameworks are much-needed tools for organizing and executing specific biology education research agendas to uncover student thinking and guide instruction (Nehm 2019). One such possible framework to examine student performance is a LP.…”

Section: Challenges In Applying Coding Approaches To Machine Learningmentioning

confidence: 99%

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Comparison of Machine Learning Performance Using Analytic and Holistic Coding Approaches Across Constructed Response Assessments Aligned to a Science Learning Progression

et al. 2020

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We systematically compared two coding approaches to generate training datasets for machine learning (ML): (i) a holistic approach based on learning progression levels and (ii) a dichotomous, analytic approach of multiple concepts in student reasoning, deconstructed from holistic rubrics. We evaluated four constructed response assessment items for undergraduate physiology, each targeting five levels of a developing flux learning progression in an ion context. Human-coded datasets were used to train two ML models: (i) an 8-classification algorithm ensemble implemented in the Constructed Response Classifier (CRC), and (ii) a single classification algorithm implemented in LightSide Researcher’s Workbench. Human coding agreement on approximately 700 student responses per item was high for both approaches with Cohen’s kappas ranging from 0.75 to 0.87 on holistic scoring and from 0.78 to 0.89 on analytic composite scoring. ML model performance varied across items and rubric type. For two items, training sets from both coding approaches produced similarly accurate ML models, with differences in Cohen’s kappa between machine and human scores of 0.002 and 0.041. For the other items, ML models trained with analytic coded responses and used for a composite score, achieved better performance as compared to using holistic scores for training, with increases in Cohen’s kappa of 0.043 and 0.117. These items used a more complex scenario involving movement of two ions. It may be that analytic coding is beneficial to unpacking this additional complexity.

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Section: Discussionmentioning

confidence: 99%

Section: Challenges In Applying Coding Approaches To Machine Learningmentioning

confidence: 99%

Comparison of Machine Learning Performance Using Analytic and Holistic Coding Approaches Across Constructed Response Assessments Aligned to a Science Learning Progression

et al. 2020

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“…Over the last three decades, a significant body of work in science education has focused on the development of assessment instruments for rigorously measuring undergraduate understanding of disciplinary core ideas (Libarkin 2008;Haudek et al 2011;Nehm 2019). CI assessments are designed to measure both normative understanding and common misconceptions in introductory science settings (Hake 1998).…”

Section: Concept Inventories In Undergraduate Biology Educationmentioning

confidence: 99%

“…Forming student groups Limit high-risk homogeneous student groupings; form diverse assemblages for class activities Distributing supplemental instructional resources Ensure high-risk students are receiving sufficient instructional resources for success Providing psychosocial supports Depending on distribution of high-risk students in a class, modulate dosage of psychosocial supports Among classes Maximizing success through accurate degree pathway placement Align high-risk students with co-enrollment course pathway options (e.g., additional recitation or discussion section) Identifying high-risk degree bottlenecks Examine links between high-risk performance patterns, course offerings, and degree completion patterns developing and using CIs to study student knowledge and misunderstandings of this topic (e.g., Kalinowski et al 2016;Nehm et al 2012;Furrow and Hsu 2019). Research has used CIs to document that students often struggle with an array of naïve ideas that are differentially evoked depending upon biological contexts (Nehm and Reilly 2007;Nehm 2019). CIs have also been used to examine the co-occurrence of naïve and scientific ideas and to study how they change throughout a semester (Opfer et al 2012;Colton et al 2018).…”

Section: Within Classmentioning

confidence: 99%

Testing the Impact of Novel Assessment Sources and Machine Learning Methods on Predictive Outcome Modeling in Undergraduate Biology

2021

Self Cite

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High levels of attrition characterize undergraduate science courses in the USA. Predictive analytics research seeks to build models that identify at-risk students and suggest interventions that enhance student success. This study examines whether incorporating a novel assessment type (concept inventories [CI]) and using machine learning (ML) methods (1) improves prediction quality, (2) reduces the time point of successful prediction, and (3) suggests more actionable course-level interventions. A corpus of university and course-level assessment and non-assessment variables (53 variables in total) from 3225 students (over six semesters) was gathered. Five ML methods were employed (two individuals, three ensembles) at three time points (pre-course, week 3, week 6) to quantify predictive efficacy. Inclusion of course-specific CI data along with university-specific corpora significantly improved prediction performance. Ensemble ML methods, in particular the generalized linear model with elastic net (GLMNET), yielded significantly higher area under the curve (AUC) values compared with non-ensemble techniques. Logistic regression achieved the poorest prediction performance and consistently underperformed. Surprisingly, increasing corpus size (i.e., amount of historical data) did not meaningfully impact prediction success. We discuss the roles that novel assessment types and ML techniques may play in advancing predictive learning analytics and addressing attrition in undergraduate science education.

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“…Tibell and Harms (2017) argue that all known key concepts can also be amalgamated under the above-mentioned core concepts. Moreover, concepts such as randomness, probability, temporal and spatial scales (sometimes referred to as threshold concepts) are interlinked with the core concepts and, thus, are particularly crucial for a deeper understanding of evolution (Fiedler et al, 2017;Nehm, 2019;Tibell & Harms, 2017). However, for this literature review, we decided to focus on the three core concepts (i.e.…”

Section: Core Concepts To Explain Evolutionary Changementioning

confidence: 99%

Identifying precursory concepts in evolution during early childhood – a systematic literature review

Bruckermann

Fiedler

Harms

2020

Studies in Science Education

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Difficulties in understanding evolution are often rooted in early childhood, arising from naïve assumptions and cognitive biases. However, literature reviews mainly focus on school and university students' understanding of evolution, with only limited comprehensive reviews on children in early childhood aged up to 7 years. This systematic review aims to capture precursory concepts in evolution and influencing cognitive biases as documented in the empirical literature. Searches of three databases identified 204 articles, of which 26 were used for further analyses after screening for eligibility. The analyses revealed that even young children are capable of understanding the basic mechanisms of core concepts in evolution, such as variation, inheritance, and natural selection. However, while children's understanding of the inheritance concept has been investigated intensively, their understanding of variation lacks in-depth research despite its probable influence on natural selection. Existing evidence is contradictory concerning the usefulness of children's cognitive biases for learning core concepts in evolution: These can serve as stepping stones for learning evolutionary principles, but their usefulness is questioned if children have already developed scientifically correct explanations. More research is clearly needed concerning the reciprocal effects of children's precursory core concepts in evolution in order to develop effective learning interventions for children.

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Biology education research: building integrative frameworks for teaching and learning about living systems

Cited by 30 publications

References 59 publications

Comparison of Machine Learning Performance Using Analytic and Holistic Coding Approaches Across Constructed Response Assessments Aligned to a Science Learning Progression

Comparison of Machine Learning Performance Using Analytic and Holistic Coding Approaches Across Constructed Response Assessments Aligned to a Science Learning Progression

Testing the Impact of Novel Assessment Sources and Machine Learning Methods on Predictive Outcome Modeling in Undergraduate Biology

Identifying precursory concepts in evolution during early childhood – a systematic literature review

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