-Lessons Addressed in the ISP Tutor-

Prerequisite: Experiment-related Vocabulary Lesson (not shown above):

  • This interactive lesson introduces students to basic vocabulary words related to experimental design and science inquiry in general, including variable; values; conditions; experiment. Students are given feedback on their responses throughout this lesson and their understanding of these concepts is assessed at the end of the lesson.

Prerequisite: Scientist vs. Engineer Lesson (not shown above):

  • Many students don’t understand what the purpose of doing experiments is. This lesson helps students understand a major difference between what a scientist and an engineer do: that scientists conduct experiments to compare things to see if they make a difference, and engineers use what scientists learn to make things that work better. They also learn about creating a controlled experiment and answer questions about a good (controlled) experimental set-up at the end of the lesson. Students can then do the experiment on their own if they wish.

#1. "Good Research Question" Lesson:

  • This lesson helps students understand what a “good” general research question is, or one that allows them to design and conduct a controlled experiment. They will learn about the proper format of a research question that includes an independent variable and dependent variable. They also gain practice considering whether the variables they’ve selected for their experiment are easily measurable.

#2. Research Question module:

  • "Worked example" mode:

    • Students view and interact with a virtual “high school student” as they work through the process of selecting a research question that is appropriate to them, based on their interests and knowledge.

    • https://www.isptutor.org/rqwe

#3. "Intro to Good Hypothesis" Lesson:

  • This lesson introduces students to the components of good hypotheses for controlled experiments, including the importance of writing a detailed and "scientific" explanation of the relationship between the independent and dependent variables. Students then evaluate sample hypotheses for quality.

    • (link TBD)

#4. Hypothesis module:

  • "Worked example" mode:

    • Students view a virtual “high school student” (of their choice) as they work through the process of developing a hypothesis after they finished their background research. In this lesson, the virtual student formulates her predictions based on her background research and sets up the detailed explanation of her prediction using the concept map feature. The virtual student demonstrates improving her detailed explanation as she learns during this process.

    • (link TBD)

  • "Guided Response" mode:

    • Students are guided in making a(n initial) specific and general prediction for their research question, and are introduced to the basic terminology and functions of concept maps, which they use to build a detailed explanation for their prediction of the relationship between the independent and dependent variables of their research question.

    • https://isp-tutor-project.github.io/demo/hypo.html

#5. Background Research module:

  • "Worked example" mode:

    • In this lesson, a "virtual “high school student” (chosen by the student) conducts research on the questions he had developed earlier. For each question, the VIN follows a cycle of (a) considering what he already knows about the question, (b) planning and searching for information, (c) reading and explaining relevant text, (d) taking notes of relevant information, and (e) summarizing the information (e.g., in text and/or pictorial form). At certain points in the lesson, “Dr. ISP” comes on-screen to ask students questions about the virtual student's actions during particular steps of the cycle, good next steps for the virtual student, or to point out other meta-cognitive actions the student is taking (e.g., planning ahead to a future inquiry stage).

    • https://isp-tutor-project.github.io/demo/games/BRMWE/

  • "Guided Response" mode:

    • The Background Research Module (BRM) is a website that consists of online units on various science concepts relevant to forming hypotheses for the research question students chose in the RQ Module. A printable “BRM worksheet” guides students through the process of conducting background research for their chosen research question in the BRM.

    • https://go.isptutor.org/brm/home/index.html

#6. Procedure & Experimental Design module (GR mode only):

  • "Guided Response" mode:

    • Students set up an experiment to address their research question. They are given immediate feedback on their responses. Students are also prompted to think through the study procedure in order to select and set up relevant variables for their experiment.

    • (link TBD)

#7. Instruction on Experimental Design (TED Tutor):

  • Students set up an experiment for their research question and are given feedback on their choices. Then students evaluate two experiments. Instruction focuses on the reason for controlling variables (students' understanding of this was found to cause robust learning and transfer).

#8. Materials module (GR mode only):

  • GR mode: Students select all materials necessary for their chosen experiment, based on the procedure.

    • (link TBD)

Prerequisite: Histogram lesson (not shown on ISP homepage above):

  • This is a prerequisite lesson to the Data Interpretation module, where hypothetical results are shown in histogram form (to show spread in the data). In this lesson, students see an example problem (dog weights), where data is first organized through sorting lowest-to-highest to identify outliers. Then students interact in the lesson to help build the histogram, which is used as a tool to identify outliers. The histogram data is used to illustrate the ideas of range and mode. Students’ understanding of the basics of interpreting histograms (mode, range, meaning of bar height) is assessed at the end of the lesson.

#9. Data Interpretation module:

  • WE mode:

    • A virtual "high school student" (chosen by the student) walks through the process of organizing (sorting) their experimental data, identifying possible outliers, summarizing the data, viewing the data in histogram form, and evaluating the results vis-à-vis their research question. The virtual HS student explains the reasons for each of these steps and consults "Dr. ISP" for help with more difficult questions (to model help-seeking behavior).

    • https://isp-tutor-project.github.io/demo/games/DIWE2/

  • GR mode:

    • Students are given a set of hypothetical results for their experiment and walk through the steps of organizing (sorting), identifying possible outliers, summarizing data, viewing the data in histogram form, and evaluating the results vis-à-vis their research question. Students’ responses in this module can determine whether they receive more intensive instruction in the DI instruction.

    • (link TBD)

  • NOTE: Results given as hypothetical results for all possible experiments are based on likely actual results (of well-designed experiments).

#10. Data Interpretation instruction:

  • This lesson walks students through organizing and summarizing data, and discusses the roles of differences in means and data spread in how confident one can be that there is an effect of the independent variable on the dependent variable. The instruction provides students with multiple opportunities to think about key concepts and builds incrementally. First, the idea of sorting data is discussed as a way to identify outliers, then the average as a good way to summarize data (when no outliers are present) is discussed, and then the idea of data spread indicating the accuracy of the average is discussed.

#11. Conclusions module:

  • Students are supported in drawing conclusions about their hypotheses based on their results and receive feedback on these responses. If the results are not consistent with students' initial hypotheses, they are prompted to consider what the likely source of the discrepancy is and, if appropriate, to conduct more background research to identify possible conceptual errors in their initial hypothesis.

    • (link TBD)

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