Assessment

We discuss transforming STEM education using three aspects: learning progressions (LPs), constructed response performance assessments, and artificial intelligence (AI). Using LPs to inform instruction, curriculum, and assessment design helps foster students’ ability to apply content and practices to explain phenomena, which reflects deeper science understanding. To measure the progress along these LPs, performance assessments combining elements of disciplinary ideas, crosscutting concepts and practices are needed. However, these tasks are time-consuming and expensive to score and provide feedback for. Artificial intelligence (AI) allows to validate the LPs and evaluate performance assessments for many students quickly and efficiently.

In this study, we used Epistemic Network Analysis (ENA) to represent data generated by Natural Language Processing (NLP) analytics during an activity based on the Knowledge Integration (KI) framework. The activity features a web-based adaptive dialog about energy transfer in photosynthesis and cellular respiration.

Integrated STEM education (iSTEM) is recognized for its potential to improve students’ scientific and mathematical knowledge, as well as to nurture positive attitudes toward STEM, which are essential for motivating students to consider STEM-related careers. While prior studies have examined the relationship between specific iSTEM activities or curricula and changes in student attitudes, research is lacking on how the aspects of iSTEM are operationalized and their influence on shifts in student attitudes towards STEM, especially when considering the role of demographic factors. Addressing this gap, our study applied multilevel modeling to analyze how different iSTEM aspects and demographic variables predict changes in student attitudes.

Integrated STEM education (iSTEM) is recognized for its potential to improve students’ scientific and mathematical knowledge, as well as to nurture positive attitudes toward STEM, which are essential for motivating students to consider STEM-related careers. While prior studies have examined the relationship between specific iSTEM activities or curricula and changes in student attitudes, research is lacking on how the aspects of iSTEM are operationalized and their influence on shifts in student attitudes towards STEM, especially when considering the role of demographic factors. Addressing this gap, our study applied multilevel modeling to analyze how different iSTEM aspects and demographic variables predict changes in student attitudes.

Integrated STEM education (iSTEM) is recognized for its potential to improve students’ scientific and mathematical knowledge, as well as to nurture positive attitudes toward STEM, which are essential for motivating students to consider STEM-related careers. While prior studies have examined the relationship between specific iSTEM activities or curricula and changes in student attitudes, research is lacking on how the aspects of iSTEM are operationalized and their influence on shifts in student attitudes towards STEM, especially when considering the role of demographic factors. Addressing this gap, our study applied multilevel modeling to analyze how different iSTEM aspects and demographic variables predict changes in student attitudes.

The purpose of this research study was to identify how teacher educators (TEs) attend to and use formative feedback as they work to support preservice teachers’ (PSTs’) learning. The formative feedback was provided to the TEs as part of recurring instructional cycles within their elementary mathematics or science methods course. In these instructional cycles, their PSTs prepared for, engaged in, and reflected on their ability to facilitate argumentation-focused discussions in a simulated classroom. After each cycle, the TEs received formative information about their PSTs’ discussion performance in the form of a feedback report and a scoring report.

We use natural language processing (NLP) to identify student ideas in science explanations. The identified ideas, along with Knowledge Integration (KI) pedagogy, informed the design of a question bank to support students during the discussion. We compare groups formed by maximizing the variety of ideas among participants to randomly formed groups. 

We use natural language processing (NLP) to identify student ideas in science explanations. The identified ideas, along with Knowledge Integration (KI) pedagogy, informed the design of a question bank to support students during the discussion. We compare groups formed by maximizing the variety of ideas among participants to randomly formed groups. 

We present a detailed empirical investigation of feature-based, recurrent neural network, and pre-trained transformer models on scoring content in real-world formative assessment data. We demonstrate that recent neural methods can rival or exceed the performance of feature-based methods. We also provide evidence that different classes of neural models take advantage of different learning cues, and pre-trained transformer models may be more robust to spurious, dataset-specific learning cues, better reflecting scoring rubrics.

We present a detailed empirical investigation of feature-based, recurrent neural network, and pre-trained transformer models on scoring content in real-world formative assessment data. We demonstrate that recent neural methods can rival or exceed the performance of feature-based methods. We also provide evidence that different classes of neural models take advantage of different learning cues, and pre-trained transformer models may be more robust to spurious, dataset-specific learning cues, better reflecting scoring rubrics.