THE IMPACT OF DIFFERENT PROOF STRATEGIES ON LEARNING GEOMETRY THEOREM PROVING

Two problem solving strategies, forward chaining and backward chaining, were compared to see how they affect students' learning of geometry theorem proving with construction. It has been claimed that backward chaining is inappropriate for novice students due to its complexity. On the other hand, forward chaining may not be appropriate either for this particular task because it can explode combinatorially. In order to determine which strategy accelerates learning the most, an intelligent tutoring system was developed. It is unique in two ways: (1) It has a fine grained cognitive model of proof-writing, which captured both observable and unobservable inference steps. This allows the tutor to provide elaborate scaffolding. (2) Depending on the student's competence, the tutor provides a variety of scaffolding from showing precise steps to just prompting students for a next step. In other words, the students could learn proof-writing through both worked-out examples (by observing a model of proof-writing generated by the tutor) and problem solving (by writing proofs by themselves). 52 students were randomly assigned to one of the tutoring systems. They solved 11 geometry proof problems with and without construction with the aid from the intelligent tutor. The results show that (1) the students who learned forward chaining showed better performance on proof-writing than those who learned backward chaining, (2) both forward and backward chaining conditions wrote wrong proofs equally frequently, (3) both forward and backward chaining conditions seldom wrote redundant or wrong statements when they wrote correct proofs, (4) the major reason for the difficulty in applying backward chaining lay in the assertion of premises as unjustified propositions (i.e., subgoaling). These results provide theoretical implications for the design of tutoring systems for problem solving

Assertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanations

Human educators possess an intrinsic ability to anticipate and seek educational explanations from students, which drives them to pose thought-provoking questions when students cannot articulate these explanations independently. We aim to imbue Intelligent Tutoring Systems with this ability using few-shot learning capability of Large Language Models. Our work proposes a novel prompting technique, Assertion Enhanced Few-Shot Learning, to facilitate the generation of accurate, detailed oriented educational explanations. Our central hypothesis is that, in educational domain, few-shot demonstrations are necessary but not a sufficient condition for quality explanation generation. We conducted a study involving 12 in-service teachers, comparing our approach to Traditional Few-Shot Learning. The results show that Assertion Enhanced Few-Shot Learning improves explanation accuracy by 15% and yields higher-quality explanations, as evaluated by teachers. We also conduct a qualitative ablation study to factor the impact of assertions to provide educator-friendly prompting guidelines for generating explanations in their domain of interest

A preliminary report on noble gases in the Kobe (CK) meteorite: A carbonaceous chondrite fell in Kobe City, Japan

We have investigated elemental and isotopic compositions of noble gases in the newly-fallen CK chondrite, Kobe. The relatively low concentrations of primordial heavy noble gases (Kr and Xe) and the relatively high ^Xe/^Xe ratio (6.51±0.02) are similar to those found in previous studies of CK chondrites. The calculated cosmic-ray exposure age based on cosmogenic ^Ne is 41Ma, and the K-Ar age is 2.1Ga. Based on calculated exposure ages and gas retention ages of Kobe and some other CK chondrites, it is likely that they have partially lost both radiogenic and cosmogenic He by solar heating during the time of exposure. Based on the ^Ar retention age, we interpret that Kobe may also have experienced thermal events, possibly related to impacts about 2 billion years age

Pharmacological Characteristics and Clinical Applications of K201

K201 is a 1,4-benzothiazepine derivative that is a promising new drug with a strong cardioprotective effect. We initially discovered K201 as an effective suppressant of sudden cardiac cell death due to calcium overload. K201 is a nonspecific blocker of sodium, potassium and calcium channels, and its cardioprotective effect is more marked than those of nicorandil, prazosine, propranolol, verapamil and diltiazem. Recently, K201 has also been shown to have activities indicated for treatment of atrial fibrillation, ventricular fibrillation, heart failure and ischemic heart disease, including action as a multiple-channel blocker, inhibition of diastolic Ca2+ release from the sarcoplasmic reticulum, suppression of spontaneous Ca2+ sparks and Ca2+ waves, blockage of annexin V and provision of myocardial protection, and improvement of norepinephrine-induced diastolic dysfunction. Here, we describe the pharmacological characteristics and clinical applications of K201

How quickly can wheel spinning be detected?

ABSTRACT We have developed a wheel spinning detector for cognitive tutors that uses a simplified method compared to existing wheel spinning detectors. The detector reads a sequence of the correctness of applying particular skill performed by a student using the cognitive tutor. The response sequence is first fed to Bayesian knowledge tracing to compute a sequence of probability of mastery at each time a skill was applied. The detector uses a neural-network model to make a binary classification for a response sequence into wheelspinning and none-wheel spinning. To test the accuracy of the detector, we validated the detector using learning interaction data taken from a school study where students used a Geometry cognitive tutor. Human coders manually tagged the data to identify wheel spinning. The results show that the neural-network based detector has high recall (0.79) but relatively low precision (0.25) when combined with Bayesian knowledge tracing that detects mastery cases. The result suggests that the neural-network based detector is practical and has a potential for scalable use such as adaptive online course where cognitive tutors are embedded into online courseware

Involvement of S-adenosylmethionine-dependent halide/thiol methyltransferase (HTMT) in methyl halide emissions from agricultural plants: isolation and characterization of an HTMT-coding gene from Raphanus sativus (daikon radish)

<p>Abstract</p> <p>Background</p> <p>Biogenic emissions of methyl halides (CH₃Cl, CH₃Br and CH₃I) are the major source of these compounds in the atmosphere; however, there are few reports about the halide profiles and strengths of these emissions. Halide ion methyltransferase (HMT) and halide/thiol methyltransferase (HTMT) enzymes concerning these emissions have been purified and characterized from several organisms including marine algae, fungi, and higher plants; however, the correlation between emission profiles of methyl halides and the enzymatic properties of HMT/HTMT, and their role in vivo remains unclear.</p> <p>Results</p> <p>Thirty-five higher plant species were screened, and high CH₃I emissions and HMT/HTMT activities were found in higher plants belonging to the Poaceae family, including wheat (<it>Triticum aestivum </it>L.) and paddy rice (<it>Oryza sativa </it>L.), as well as the Brassicaceae family, including daikon radish (<it>Raphanus sativus</it>). The in vivo emission of CH₃I clearly correlated with HMT/HTMT activity. The emission of CH₃I from the sprouting leaves of <it>R. sativus</it>, <it>T. aestivum </it>and <it>O. sativa </it>grown hydroponically increased with increasing concentrations of supplied iodide. A gene encoding an <it>S</it>-adenosylmethionine halide/thiol methyltransferase (HTMT) was cloned from <it>R. sativus </it>and expressed in <it>Escherichia coli </it>as a soluble protein. The recombinant <it>R. sativus </it>HTMT (RsHTMT) was revealed to possess high specificity for iodide (I^-), bisulfide ([SH]^-), and thiocyanate ([SCN]^-) ions.</p> <p>Conclusion</p> <p>The present findings suggest that HMT/HTMT activity is present in several families of higher plants including Poaceae and Brassicaceae, and is involved in the formation of methyl halides. Moreover, it was found that the emission of methyl iodide from plants was affected by the iodide concentration in the cultures. The recombinant RsHTMT demonstrated enzymatic properties similar to those of <it>Brassica oleracea </it>HTMT, especially in terms of its high specificity for iodide, bisulfide, and thiocyanate ions. A survey of biogenic emissions of methyl halides strongly suggests that the HTM/HTMT reaction is the key to understanding the biogenesis of methyl halides and methylated sulfur compounds in nature.</p

Students' Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming

The rapid evolution of artificial intelligence (AI), specifically large language models (LLMs), has opened opportunities for various educational applications. This paper explored the feasibility of utilizing ChatGPT, one of the most popular LLMs, for automating feedback for Java programming assignments in an introductory computer science (CS1) class. Specifically, this study focused on three questions: 1) To what extent do students view LLM-generated feedback as formative? 2) How do students see the comparative affordances of feedback prompts that include their code, vs. those that exclude it? 3) What enhancements do students suggest for improving AI-generated feedback? To address these questions, we generated automated feedback using the ChatGPT API for four lab assignments in the CS1 class. The survey results revealed that students perceived the feedback as aligning well with formative feedback guidelines established by Shute. Additionally, students showed a clear preference for feedback generated by including the students' code as part of the LLM prompt, and our thematic study indicated that the preference was mainly attributed to the specificity, clarity, and corrective nature of the feedback. Moreover, this study found that students generally expected specific and corrective feedback with sufficient code examples, but had diverged opinions on the tone of the feedback. This study demonstrated that ChatGPT could generate Java programming assignment feedback that students perceived as formative. It also offered insights into the specific improvements that would make the ChatGPT-generated feedback useful for students

Laparoscopic Hepatectomy for the Patient with Hemophilia A with High Titer Factor VIII Inhibitor

We present the first case of laparoscopic left lateral segmentectomy for hepatocellular carcinoma (HCC) in a patient with hemophilia A, acquired hepatitis C, and high-titer factor VIII inhibitor, which was confirmed by preoperative diagnosis. He underwent laparoscopic left lateral segmentectomy with the administration of recombinant activated factor VII. Surgery could be performed with reduced intraoperative hemorrhage. He experienced postoperative intra-abdominal wall hemorrhage, which was successfully managed with red cell concentrates transfusion and administration of recombinant activated factor VII. Laparoscopic hepatectomy can be applied for hemophilia patients with high titer inhibitors