Volume 46, Number 1

Dynamic Earth

Volume 46, Number 1

Dynamic Earth

Volume 46, Number 1

Dynamic Earth

Volume 90, Number 1

Alternative Assessments in the Science Classroom

When I say “assessments,” what immediately comes to your mind? Tests, quizzes, and exams? Does it always have to be that way?

As you know, there are two main categories of assessments.

Volume 90, Number 1

Alternative Assessments in the Science Classroom

When I say “assessments,” what immediately comes to your mind? Tests, quizzes, and exams? Does it always have to be that way?

As you know, there are two main categories of assessments.

Volume 90, Number 1

Alternative Assessments in the Science Classroom

When I say “assessments,” what immediately comes to your mind? Tests, quizzes, and exams? Does it always have to be that way?

As you know, there are two main categories of assessments.

We propose cross-disciplinary learning as a construct that can guide instruction and assessment in programs that feature sequential learning across multiple science disciplines. Cross-disciplinary learning combines insights from interdisciplinary learning, transfer, and resources frameworks and highlights the processes of resource activation, transformation, and integration to support sense-making in a novel disciplinary context by drawing on knowledge from other prerequisite disciplines.

We propose cross-disciplinary learning as a construct that can guide instruction and assessment in programs that feature sequential learning across multiple science disciplines. Cross-disciplinary learning combines insights from interdisciplinary learning, transfer, and resources frameworks and highlights the processes of resource activation, transformation, and integration to support sense-making in a novel disciplinary context by drawing on knowledge from other prerequisite disciplines.

Over the past decade, researchers have developed several teaching observation protocols for use in higher education, such as the Teaching Dimensions Observation Protocol (TDOP), Classroom Observation Protocol for Undergraduate STEM (COPUS), Practical Observation Rubric to Assess Active Learning (PORTAAL), and Decibel Analysis for Research in Teaching (DART). Choosing a protocol for a particular need can seem daunting.

Over the past decade, researchers have developed several teaching observation protocols for use in higher education, such as the Teaching Dimensions Observation Protocol (TDOP), Classroom Observation Protocol for Undergraduate STEM (COPUS), Practical Observation Rubric to Assess Active Learning (PORTAAL), and Decibel Analysis for Research in Teaching (DART). Choosing a protocol for a particular need can seem daunting.

There are more STEM jobs than there are qualified graduates to fill these positions, and recruiting students into STEM majors is insufficient. Of students who enter college intending to pursue STEM, nearly half do not finish their STEM degrees. In this article, we focus on retaining students who enter college with a declared biology major. This qualitative study examines this retention issue through the lens of identity theory, situated learning, and constructivism in the context of a research-focused biology first-year seminar at a small, private university.

There are more STEM jobs than there are qualified graduates to fill these positions, and recruiting students into STEM majors is insufficient. Of students who enter college intending to pursue STEM, nearly half do not finish their STEM degrees. In this article, we focus on retaining students who enter college with a declared biology major. This qualitative study examines this retention issue through the lens of identity theory, situated learning, and constructivism in the context of a research-focused biology first-year seminar at a small, private university.

Students reasoning with data in an authentic science environment had the opportunity to learn about the process of science and the world around them while developing skills to analyze and interpret self-collected and secondhand data. Our results show that nearly 50% of the treatment group responses were accurate when describing the reason for measuring water parameters, compared with 26% in the traditional lab group.

Students reasoning with data in an authentic science environment had the opportunity to learn about the process of science and the world around them while developing skills to analyze and interpret self-collected and secondhand data. Our results show that nearly 50% of the treatment group responses were accurate when describing the reason for measuring water parameters, compared with 26% in the traditional lab group.