Biology O Level Questions And Answers
Janise Knollman
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There have many stories published since ChatGPT came out last November about the potential of college students using the AI to write essays, answer exam questions, and otherwise skirt the educational honor system.
After a decade of teaching introductory biology at the college level, I find it more difficult with each passing semester to come up with new ideas for quiz questions each week. So after playing with ChatGPT with my kids and asking it to write stories and songs about hamsters, Fortnite, and John Cena, I decided to ask it if it can write some biology questions for me. The following is what I found out (TLDR: yes, ChatGPT can write some pretty darn good biology questions).
OK check, ChatGPT is knowledgeable of Bloom's. Notice that it used specific tenses of the six Bloom's levels (remembering, understanding, applying, analyzing, evaluating, and creating) so I used those specific verbs when asking it to write questions for me. Next I picked a topic that I would hope it was familiar with (Darwin's theory of natural selection) and asked it write me a Bloom's remembering level multiple choice question.
Goodness gracious, not only can ChatGPT write a multiple choice question, but it tells me the correct answer, and I would definitely classify this as a remembering level question. Moving on to the level of understanding.
Alright, that is a decent understanding question. But now let's really challenge ChatGPT. Applying and analyzing questions usually require students to transfer their knowledge to new situations or scenarios. Let's see how it did.
I almost fell out of my chair when I read these. Not only did ChatGPT up the game and write true application and analysis questions, but it gave novel scenarios for students to assess without being prompted to do so!
Now to the highest two levels, evaluating and creating. Putting aside the argument for a moment that some make that it isn't possible to write these levels of multiple choice questions, let's see how ChatGPT fared.
Well my oh my. ChatGPT definitely worked at higher levels and asked questions that assess students' abilities to evaluate different explanations and to come up with new experimental designs to test a hypothesis. I'm not a fan of "all of the above" options, but I won't nit pick too much now.
So while ChatGPT can write some pretty good multiple choice questions of all six Bloom's levels, it cannot write questions that incorporate figures or drawings, data tables, or graphs from scientific publications, all of which I use to further assess my students' scientific analysis abilities. It also has a hard time writing complex, open-ended, numerical-based problems, of which I use in my chemical engineering courses (but it seems to do a pretty good job at writing straightforward numerical problems, think plug and chug). But text-only multiple choice questions are very commonly used on quizzes and exams in STEM courses (including by me), so now the question has to be asked - should we as instructors use ChatGPT to help us write assessments?
I don't know if I have a strong feeling about this yet, but it sure is tempting to think about. Is using ChatGPT to write questions any different than borrowing old questions from a colleague, using the test bank of questions that comes with your textbook, or googling for questions that are posted online? In all of these cases, you obtain a question that is ready to use, and you may use it verbatim, but you may just as likely edit it to fit your style. So perhaps that is the way to go with ChatGPT - ask it to write you a question and use its response as a foundation to which you edit and make the question more of your own. Or maybe we don't give in to the AI and keep our questions 100% human-generated.
What do you think? Is using ChatGPT to write exam questions fair, ethical, or justifiable? Should you tell your students if you do choose to use ChatGPT to write questions? Please comment below or email me and share your thoughts, would love to hear what you think!
Justin Shaffer is a teaching associate professor in chemical and biological engineering and in quantitative biosciences and engineering at the Colorado School of Mines and is the the founder of Recombinant Education. This article may or may not have been written by ChatGPT.
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OCR A-Level Biology past papers are previous exam papers that have been used in OCR A-Level Biology exams. They provide students with an opportunity to practice and prepare for their upcoming exams by familiarizing themselves with the format, content, and style of questions asked in the OCR A-Level Biology exams.
OCR A-Level Biology past papers are important because they allow students to understand the structure and content of the OCR A-Level Biology exams. They also help students to practice answering questions in the same format as the real exam, which helps to improve their exam technique and confidence.
OCR A-Level Biology past papers can be found on the OCR website or on various educational websites that provide free resources for students. It is also possible to purchase printed copies of past papers from online retailers or bookstores.
It is recommended that students practice as many past papers as possible to improve their understanding of the exam format and content. Students should aim to complete at least 3-4 past papers per topic or unit and review their answers carefully to identify areas for improvement.
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We all know that bioinformatics and computational biology are here to stay and that their impact on scientific thought and direction will be increasing in the future. Since there is a lot of interdisciplinary research being undertaken by folks who participate in this forum, I am curious as to what big and interesting biological problems folks think will be best solved either directly by computational approaches or in an integrated computational and bench science environment. Of course, I know that the answer to this is "everything", but I am really curious about specific questions in your field of interest.
*Tip of the Week: Caleydo for gene expression and pathway visualization I wish I had a tool with 5 planes where I could put all the resources I use and visualize them at once. And connect pieces somehow.
But it struck me again most recently on the mitochondrial transcriptome. In mitochondria you need both nuclear and mitochondrial genes in the same space, but in no current browser can you really consider both genomes, you know?
More specifically I think a number of answers in cancer biology are going to come out of the big sequencing projects. But those may be mechanism solutions and not cures at this point, though. I would like to see that progress very much, I think that's what taxpayers really want from us.
As we identify sequence variation/mutation that is correlated with phenotype, one 'next question' that emerges is how these variations affect the molecular function of the gene at the protein or possibly RNA level. For example, if a gene is recurrently mutated in a disease by a non-conservative amino acid substitution. Does this result in gain-of-function or loss-of-function? How has this function been conferred or lost? What is it about the 3D structure, interaction partners, dimerization potential, etc. of the protein that is changed by mutation? Does this change alter the way small molecule inhibitors will interact with the protein? Does it suggest the possibility of a novel drug? How can we predict what that drug might look like without random compound library screening in the lab?
All of these questions relate to the fairly old structure-function relationship problem of molecular biology. Currently very expensive and lengthy wet lab work is required to address them. But we need to be addressing these problems computationally to be more systematic and speed up discovery and clinical translation...
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