学部・大学院区分
Undergraduate / Graduate | | 理学部 | | 時間割コード
Registration Code | | 0680170 | | 科目区分
Course Category | | 専門科目
Specialized Courses | | 科目名 【日本語】
Course Title | | [G30]統計物理学２ | | 科目名 【英語】
Course Title | | [G30]Statistical Physics II | | コースナンバリングコード
Course Numbering Code | | | | 担当教員 【日本語】
Instructor | | WOJDYLO John Andrew ○ | | 担当教員 【英語】
Instructor | | WOJDYLO John Andrew ○ | | 単位数
Credits | | 2 | | 開講期・開講時間帯
Term / Day / Period | | 秋 金曜日 ５時限
Fall Fri 5 | | 授業形態
Course style | | 講義
Lecture | | 学科・専攻
Department / Program | | | | 必修・選択
Compulsory / Selected | | | |
授業の目的 【日本語】
Goals of the Course(JPN) | | | | 授業の目的 【英語】
Goals of the Course | | In this course, students will gain a thorough grounding in Thermodynamics and Statistical Mechanics, enabling them to continue to further studies next semester in Statistical Physics III, as well as to solve advanced problems in Thermodynamics and intermediate problems in the Canonical and Microcanonical Formalisms.
This is a challenging course that requires a substantial time commitment.
Statistical Mechanics is one of the major fields of physics: around 30% of Physics Nobel Prizes have been awarded for discoveries directly or indirectly related to Statistical Mechanics, particularly quantum many-body systems, many-body systems in the classical limit, other condensed matter physics, phase transitions and field theories. The principles and methods are applicable in many fields of physics, including condensed matter physics (e.g. Bose Einstein Condensation, superconductivity, materials science) and high energy physics (spontaneous symmetry breaking, lattice gauge field theories, the Higgs Mechanism (which is a type of phase transition) and so on) as well as astrophysics (neutron stars, simulations of galaxy evolution, and so on). The principles and methods are also applicable in a very wide variety of fields outside physics, such as biology, neuroscience, modelling of pandemics, network theory, machine learning and artificial intelligence. G30 students in the past have shown that students in chemistry, chemical engineering and materials science with a thorough grounding in the principles of Statistical Mechanics have a significant advantage over their peers who do not possess this grounding.
This unit is the first half of a full-year course. After learning the mathematical structure of thermodynamics and how thermodynamics works -- with many examples of systems beyond the ideal gas -- students are introduced to equilibrium statistical mechanics, which describes the equilibrium conditions of systems consisting of a large number of particles. Applications are considered in condensed matter physics, solid state physics, cosmology, chemistry, materials science and biology. Problem-solving is an integral part of the course: students should attend fortnightly tutorials (Physics Tutorial IIIa) where they will discuss many of the assignment questions and receive hints for solutions. Weaker students are particularly encouraged to attend tutorials and submit assignments. This semester students are thoroughly prepared for quantum statistical mechanics in SP3 next semester. It is recommended that students take Quantum Mechanics II concurrently.
At the end of Statistical Physics III next semester students will be adequately prepared with regards to their knowledge of statistical mechanics and thermodynamics to undertake further studies in S-lab, R-lab, TB-lab, E-lab, H-lab, QG-lab and other, including experimental, labs in both the Department of Physics and Department of Applied Physics, as well as chemistry and computational biology labs at Nagoya University. A knowledge of statistical mechanics (quantum and classical) is essential for students interested in experimental physics, theoretical physics, chemistry and mathematical biology.
This 2nd course in quantum mechanics is the first half of a full-year Year 3 course. The main goal of this course is first to make von Neumann's Hilbert Space approach to quantum mechanics seem intuitively simple to the student; then to enhance their understanding of Nature by considering topics including:
• why the force felt by a quantum particle is different to the force felt by a classical particle in the same potential, and what the conditions of the quantum-classical crossover are;
• what the spread of possible paths taken by a quantum particle in the neighbourhood of a classical path is likely to be, and what the conditions of the quantum-classical crossover are;
• various manifestations of the classical limit being reached in the limit of high quantum number, even when the system is inherently quantum mechanical (such as the simple harmonic oscillator);
• interference effect between identical particles in the quantum mechanical limit and the contrast with distinguishable particles;
• correlation of measurements of entangled particles and why this implies non-locality (via the CHSH (Clauser-Horne-Shimony-Holt) Inequality);
• quantum teleportation and other "spooky" quantum effects that have been confirmed by experiment.
Through learning how to represent fascinating phenomena such as these mathematically, students learn to express with ease their new physical intuition using mathematical language: the mathematical language becomes second nature and students learn not to be overwhelmed by the mathematical symbols and to discern the simple physical principles expressed by them. By breaking down seemingly complex calculations in strange new territory into many simple steps -- by building the habit of working through each line in the textbook and testing and firming up their understanding by solving numerous problems -- students extend their problem-solving and theory-building abilities. This approach also instills critical thinking, as students make it a habit to verify statements for themselves and not just believe everything they are told.
This is a challenging course intended mainly for students who wish to pursue research in theoretical physics. Students need to be strong in both physics and mathematics and be prepared to work hard if they want to succeed in it.
At the end of Quantum Mechanics III next semester students will be adequately prepared with regards to their knowledge of quantum mechanics to undertake further studies in St-lab, Sp-lab, J-lab, R-lab, TB-lab, E-lab, H-lab, QG-lab and other, including experimental, labs in both the Department of Physics and Department of Applied Physics (e.g. Tanaka Lab), as well as chemistry and computational biology labs at Nagoya University. For chemistry majors, Quantum Mechanics II and III provide a powerful boost to their skills-set which can open many doors.
NOTICE TO PROSPECTIVE NUPACE (exchange) STUDENTS
Prospective exchange students should be aware that the Nagoya University Department of Physics screens incoming exchange students and rejects or accepts them irrespective of whatever assurances they have been given by their home university or Nagoya University staff in other departments. This means it is possible that you have come all this way to Japan with the intention of continuing your undergraduate degree in Physics but will not be permitted to do so.
If you are interested in coming to study Physics at Nagoya University in the G30 Undergraduate Physics Program then you should contact the lecturer of this course before you make the decision to come to Japan.
WARNING ABOUT THE USE OF GENERATIVE AI
• Assignment exercises are an essential part of both the lecture course and the associated tutorial course. Students are thereby given the opportunity to master a wide variety of technical variations encountered in solving concrete examples of the basic theory, as well as a variety of simple extensions of the basic theory, and simple analytical techniques related to the lecture content but not mentioned in lectures, that extend the scope of problems that students can solve. Mastery of these aspects makes students more attractive to employers or future research labs.
• Reliance on generative AI is harmful to learning. In view of the importance of mastering the assignment exercises, measures will be put in place to ensure that students who rely on generative AI and group discussions to attain high assignment scores without mastering the content will not achieve an inflated final grade: inflated grades are unfair to students who take learning seriously and achieve scholarly independence by genuine hard work. After completing this course, if they do use AI to help with study, students should aim to be capable of critiquing the output from generative AI and evaluating its correctness -- in other words, they must be capable of solving the exercises as well as creative variations of the exercises and lecture content with no help. No employer wants employees who are unable to do this.
Face-to-face lectures, tutorials and exams are opportunities to demonstrate your scholarly independence. Even students who find mathematics difficult can attain at least a "B" grade by engaging in good study habits and learning the lecture content well, and by being honest with themselves and others.
All lecture course exams (Midsemester, End of Semester) are in-class. Students who rely on generative AI and do not master the content will reveal themselves when they are unable to make any meaningful progress with exam questions that are simple repetitions of assignment questions with minor changes or simple creative variations. Even students who have difficulty with mathematics can master such questions if they have good study habits and perseverence. Excessive reliance on generative AI and group discussions wastes time, harms your learning and affects your grades. |
| | 到達目標 【日本語】
Objectives of the Course(JPN)) | | | | 到達目標 【英語】
Objectives of the Course | | Objectives of the Course
The overriding objective of this course is to foster the student's scholarly independence. In order to succeed in this course, students must understand the content sufficiently to have the ability to potentially critique and evaluate the correctness of the output from group work, and generative AI if they use it, relevant to the lecture and assignment content. This is a transferable, real-world skill and, more importantly, mindset: the habit of checking before believing, while possessing the knowledge and technical ability to make the judgement.
Students will have the ability to solve problems such as creative variations of assignment exercises and lecture content with no help, and thereby demonstrate that they are capable of learning STEM knowledge and techniques, and are therefore potentially useful to future employers or university research groups. |
| | 授業の内容や構成
Course Content / Plan | | Course Outline
Callen Chapts 1-8, 15-17, 21 (some parts omitted); Reif Chapts 1-3, 6-7, Appendix A12. Some topics are more fully explored in tutorials. All lectures are recorded and made available in MS Teams immediately after the lecture.
Lecture 1. Fundamental Relation, Entropy Representation; Postulates of the Entropy. Partial derivatives and experiments. Thermodynamic coordinates. Existence of the internal energy thermodynamic potential. Existence of an entropy function of state -- proof from within thermodynamics. Basic postulates of thermodynamics. Nernst Postulate (3rd Law of Thermodynamics.) Fundamental relation in the Entropy representation. Based on Callen Chapter 1; and Zemansky and Dittman Chapter 2.
Lecture 2. Top-Down Approach: Equations of State from the Fundamental Relation. Examples. Extensive parameters are homogeneous order 1. Intensive parameters are homogeneous order 0. Thermal and Mechanical Equilibrium. Euler relation. Gibbs-Duhem relation. Based on Callen Chapter 2.
Lecture 3. Bottom Up Approach: Fundamental Relation from the Equations of State. Mathematical theorems underlying thermodynamics. Examples of applying compatibility condition, Gibbs-Duhem Relation, Euler Relation, 1st Law in molar form. 2nd Equation of State from van der Waals Equation of State. Example: rubber band; photon gas; Fundamental Relation for one-component ideal gas. Ideal gas: "Gibbs Paradox"? Entropy of a mixture: “entropy of mixing”. Molar heat capacity and other derivatives. Based on Callen Chapter 3.
Lecture 4. The Maximum Work Theorem. Possible and impossible processes. Quasistatic and reversible processes – how can temperature be increased reversibly? Heat flow and coupled systems. The maximum work theorem (proof without using Carnot cycle). Carnot Efficiency. Carnot cycle: why is it necessary? Carnot cycle for a photon gas.
Lecture 5. Thermodynamic potentials and their physical interpretation. The Legendre transform. Thermodynamic potentials: internal energy, Helmholtz free energy, Gibbs free energy, enthalpy. Based on Callen Chapter 5.
Lecture 6. The Extremum Principle in the Legendre Transformed Representations. Physical meaning of the potentials: a first look. Minimum principles. Applications of thermodynamics. How to measure the entropy. How to liquefy gases -- throttling (Joule-Thompson process). Based on Callen Chapter 6.
Lecture 7. Maxwell's relations and applications. Algorithm for reducing thermodynamic derivatives to a combination of easily-measurable quantities. Applications: adiabatic compression; isothermal expansion; free expansion. Based on Callen Chapter 7.
Lecture 8. Introduction to Statistical Mechanics: the “flavour” of SM. Intuitive introduction to the SM of “isolated” systems: i.e. entropy representation – microcanonical ensemble (semi-classical treatment). Why are predictions possible at all? Central Limit Theorem. Postulate of equal a priori probability. Importance of the number of accessible states Omega. Some examples of counting Omega: Einstein model of a crystalline solid; the “two-state model” and the Schottky Hump. Entropy and Omega. Equilibrium conditions. Combinatorial methods for counting Omega are usually difficult and of limited use – two ways to overcome this: 1) take advantage of high dimensionality [today]; 2) use Legendre transformed representations [later]. Based on Callen Chapter 15.
Lecture 9. Basic Facts About the Binomial Distribution. How a Gaussian emerges from the binomial distribution; mean, variance, standard deviation, etc. Reif Chapter 1. NONEXAMINABLE: Theory of the Classical Microcanonical Ensemble: Gibbs Ensemble; Classical Liouville Theorem; role of symmetry in Statistical Mechanics. Classical microstates. The density function and thermodynamic averages. Classical Ergodic Theorem. (Huang p. 52-54; 62-65; 127-135). The density function is a conserved quantity, therefore it must be expressible as a linear combination of fundamental additive conserved quantities. (Lifshitz and Pitaevski p. 11.)
Lecture 10. Evolution of an Isolated System to Equilibrium – Entropy. Why do many kinds of isolated systems tend to equilibrium; why do systems evolve out of equilibrium states? Why do systems in an ensemble want to occupy each possible state equally; why do probability distributions want to be flat, thus maximizing our ignorance of the system state? Why does entropy increase in a spontaneous process? Why is entropy maximum at equilibrium? Why S=k Log(Omega)? Why S= -k Sum{pr Log pr}? Principle of Detailed Balance; Boltzmann’s H-Theorem. (Reif A12.)
Lecture 11. Statistical Mechanics in the Helmholtz Representation: Canonical formalism. Canonical partition function. Boltzmann probability distribution derived for a system in contact with a heat reservoir. Connection with thermodynamics. Weakly-interacting systems: additivity of energies and factorizability of the partition function. Basic examples. (Callen 16-1, 16-2.)
Lecture 12. Canonical Partition Function cont’d. Fluctuations. Adiabatic work. Microscopic effect of work and heat. In equilibrium, energy probability distribution is Gaussian: overwhelming probability that the system is within 1 standard deviation of the mean. 3rd Law of Thermodynamics. If the mean energy of a system is known then the canonical ensemble applies (temperature is fixed). Proof: allow energy of the system to fluctuate and use Method of Lagrange Multipliers to find the MOST PROBABLE probability distribution, which is Boltzmann's distribution. (Reif Ch. 6.) Other kinds of partition function: equilibrium distribution for systems in contact with a particle reservoir or volume reservoir; equivalence between all these distributions (away from a phase transition) due to high dimensionality (Hill, Ch. 1,2); a constructive, physical proof of equivalence (van Kampen).
Lecture 13. Paramagnetism. We solve the problem of paramagnetism first for the spin-1/2 case then for the arbitrary spin case. (Reif p. 206-208; 257-262.) Bohr-van Leeuwen Theorem. (Kubo, p. 138.) NONEXAMINABLE: 1st Law of Thermodynamics for Magnetic Systems: role of a pair of intensive and extensive parameters is swapped (Kittel Chapt. 18; Callen Appendix B).
Lecture 14. The Classical Limit. Why is our approach classical even though we write the partition sum as a discrete sum over states (with many-body energy eigenvalues Er)? Pauli Exclusion Principle and the enumeration of states. A combinatorial approach to proper counting: derivation of the quantum distribution functions. The Bose-Einstein and Fermi-Dirac Distributions tend to the Boltzmann Distribution in the “classical limit” – why? What characterizes the classical limit? The essential condition: large quantum numbers; implication for the PEP as well as for simultaneous measurement of canonically conjugate observables. Example: translational and internal modes (vibrational, rotational etc. modes) of a diatomic ideal gas (Callen 16-3). Symmetry number. How the classical partition function emerges from the semi-classical partition function. Classical ideal gas: equations of state, entropy (Reif 7.2, 7.3 p 239-246). The classical equipartition theorem (Reif 7.5 p 248-250).
Lecture 15. 1. Kinetic Theory of Dilute Gases in Equilibrium. Maxwell velocity distribution. Distribution of a component of velocity. Speed distribution: mean, rms, most probable speeds. (Reif 7.9, 7.10). NONEXAMINABLE: 2. Fundamental Postulates of Quantum Statistical Mechanics: in an isolated system, equal a priori probabilities and the postulate of random phase. The operation of taking a statistical average and quantum average simultaneously: the density operator. Properties of the density operator. Canonical partition function in terms of the density operator. Why the off-diagonal entries of the density operator must be zero – this leads to the random phase postulate. Significance of the de Broglie wavelength of a molecule in an ideal gas in equilibrium at temperature T. |
| | 履修条件
Course Prerequisites | | Prerequisites:
Calculus I; Calculus II; Fundamentals of Physics I; Fundamentals of Physics II; or Consent of Instructor.
Students must be strong in physics and mathematics. |
| | 関連する科目
Related Courses | | | Quantum Mechanics II; Physics Tutorial IIIa; Statistical Physics III (next semester). It is strongly advised that students concurrently enroll in Physics Tutorial IIIa. |
| | 「履修取り下げ届」提出の要・不要
Necessity / Unnecessity to submit "Course Withdrawal Request Form" | | | For students who wish to withdraw, a formal withdrawal form must be signed by the lecturer and submitted to the Student Office by the deadline in November. |
| | 履修取り下げの条件等
Conditions for Course Withdrawal | | Conditions for Course Withdrawal
A withdrawal request made after the deadline in November will be rejected unless the circumstances are very exceptional.
If Statistical Physics II is NOT A COMPULSORY SUBJECT and the student plans never to take Statistical Physics II in the future, then a late withdrawal request will be considered. |
| | 成績評価の方法と基準
Course Evaluation Method and Criteria | | Course Evaluation Method and Criteria
Attendance and class performance, attitude: 5%; Weekly quizzes or other written assessment: 30%; Midterm exam: 32.5%; Final Exam: 32.5%.
Some exam questions in the lecture course are simple repetitions of either tutorial or lecture assignment questions with minor changes or simple creative variations. If the student is unable to make meaningful progress with such exam questions during the exam, while in the assignment they presented quite a good solution, then the entire assignment corresponding to that question will be deemed to have a mark of 20%.
A certain subset of both Lecture Assignments and Tutorial Assignments must be submitted within a week of the midsemester exam. They will not be accepted after this, and will receive zero marks.
Be aware that mastering the assignments in both the tutorial course and lecture course is essential for performing well in the SP2 lecture course exams. You should view assignment work as preparation for the exams.
Students will only be permitted to sit the End of Semester Exam if they have submitted at least 4 out of 7 assignments by the date on which Lecture 15 is held.
Irrespective of how high the total assignment score is, in the two exams the student must demonstrate a sufficient understanding of all the basics: in particular, they must do well in the exam questions that are similar to certain assignment questions or that cover certain simple points in the lecture notes. Therefore, in principle, a student cannot pass the course if they score less than 45% on average in the two exams, even if they score 100% overall in assignments. |
| | 不可(F)と欠席(W)の基準
Criteria for "Fail (F)" & "Absent (W)" grades | | Criteria for "Fail (F)" & "Absent (W)" grades
The "Absent (W)" grade is reserved for students who withdraw by November 16. After that day, a letter grade will be awarded based on marks earned from all assessment during the semester.
If Statistical Physics II is NOT A COMPULSORY SUBJECT and the student plans never to take Statistical Physics II in the future, then a late withdrawal request will be considered. |
| | 参考書
Reference Book | | Reference Books
1. Hill, T., An Introduction to Statistical Thermodynamics, Dover, 1986. (Excellent introduction to Statistical Mechanics at Year 3 level. Alternative textbook. Highly recommended. Cheap to buy.)
2. Zemansky, M.W. and Dittman, R.H., Heat and Thermodynamics, An Intermediate Textbook, McGraw-Hill, 1992. (Excellent for empirical basis of thermodynamics.)
3. Kittel, C., Elementary Statistical Physics, Dover, 2004. (Highly recommended. Cheap to buy.)
4. Kubo R., Statistical Mechanics, North Holland, 1965. (More of a second course on Stat Mech, but contains many examples and worked solutions.)
5. Huang, K., Statistical Mechanics, Wiley. (Advanced reference.)
6. Kittel, C. and Kroemer, H., Thermal Physics, W.H. Freeman. (Try as alternative.)
7. Landau, L.D. and Lifshitz, E.M., Statistical Physics, Part I, by E.M. Lifshitz and L.P. Pitaevskii, Pergamon Press. (A classic book: thorough, advanced treatment.) |
| | 教科書・テキスト
Textbook | | Textbooks
1. Callen, H., Thermodynamics and an Introduction to Thermostatistics, 2nd ed.,Wiley, 1985.
(The central textbook in this course. Japanese translation has fewer typographical errors.)
2. Reif, F., Fundamentals of Statistical and Thermal Physics, McGraw-Hill, 1965. |
| | 課外学習等（授業時間外学習の指示）
Study Load(Self-directed Learning Outside Course Hours) | | Study Load (Self-directed Learning Outside Course Hours)
This is a challenging course that requires a substantial time commitment.
You are expected to revise the lecture notes, read the textbook, and solve assignment problems outside lecture hours. You cannot learn physics by only attending lectures. The exams will consist of questions covering both lecture notes and assignments.
• Focus on your undergraduate studies. Resist distractions. Learn as much as you can. This period in your life is a golden opportunity that might never be repeated.
• It is strongly advised that students concurrently enrol in Physics Tutorial IIIa.
• To get the full benefit of this unit, students should enrol in Statistical Physics III next semester. To get the full benefit of Statistical Mechanics, students should also enrol in Quantum Mechanics II, and Quantum Mechanics III next semester. The next level up -- Year 4 or graduate level -- is Condensed Matter Field Theory, which has as its starting point the first half of QMIII, parts of QMII, and all of SPIII. |
| | 注意事項
Notice for Students | | • Some exam questions in the lecture course are simple repetitions of either tutorial or lecture assignment questions with minor changes or simple creative variations. If the student is unable to make meaningful progress with such exam questions during the exam, then the entire assignment corresponding to that question will be deemed to have a mark of 20%.
• A certain subset of both Lecture Assignments and Tutorial Assignments must be submitted within a week of the midsemester exam. They will not be accepted after this, and will receive zero marks.
• Students will only be permitted to sit the End of Semester Exam if they have submitted at least 4 out of 7 assignments by the date on which Lecture 15 is held.
• Irrespective of how high the total assignment score is, in the two exams the student must demonstrate a sufficient understanding of all the basics: in particular, they must do well in the exam questions that are similar to certain assignment questions or that cover certain simple points in the lecture notes. Therefore, in principle, a student cannot pass the course if they score less than 45% on average in the two exams, even if they score 100% overall in assignments.
WARNING ABOUT THE USE OF AI
• Despite any rules and better ethical judgment, many of you will rely on AI (ChatGPT, Gemini, Sonnet, Claude etc.) to solve your assignment problems. Be aware that besides constituting academic misconduct and dishonesty, such use of AI is detrimental to your learning: among other things, you will lack the experience of getting stuck and digging your way out of a problem using your own knowledge, tenacity, ingenuity and creativity. You will not learn resilience in the field of study. The examinations are designed to test the quality of your learning, your ability to bring together multiple threads dealt with in lectures and assignments, your ability to interpret the equations and unify your understanding, to seek connections and dig your way out of a seeming dead end when solving a problem. You will not learn this by relying on AI. You will do very well in assignments but very poorly in the exams.
• Reliance on generative AI is harmful to learning. In view of the importance of mastering the assignment exercises, measures will be put in place to ensure that students who rely on generative AI and group discussions to attain high assignment scores without mastering the content will not achieve an inflated final grade: inflated grades are unfair to students who take learning seriously and achieve scholarly independence by genuine hard work. After completing this course, if they do use AI to help with study, students should aim to be capable of critiquing the output from generative AI and evaluating its correctness -- in other words, they must be capable of solving the exercises as well as creative variations of the exercises and lecture content with no help. No employer wants employees who are unable to do this.
Face-to-face lectures, tutorials and exams are opportunities to demonstrate your scholarly independence. Even students who find mathematics difficult can attain at least a "B" grade by engaging in good study habits and learning the lecture content well, and by being honest with themselves and others. |
| | 他学科聴講の可否
Propriety of Other department student's attendance | | | Students from any department are welcome as long as they have the necessary mathematical grounding. They should also have a strong interest in physics. |
| | 他学科聴講の条件
Conditions for Other department student's attendance | | | Students from any department are welcome as long as they have the necessary mathematical grounding. They should also have a strong interest in physics. |
| | レベル
Level | | | | キーワード
Keyword | | | | 履修の際のアドバイス
Advice | | Advice
• Focus on your Year 3 undergraduate physics studies, as these are difficult courses. Resist distractions, particularly noncore lecture courses that you can take in another year when you have more time. Learn as much physics as you can. This period in your life is a golden opportunity that might never be repeated.
• It is strongly advised that students concurrently enrol in Physics Tutorial IIIa.
• To get the full benefit of this unit, students should enrol in Statistical Physics III next semester. To get the full benefit of Statistical Mechanics, students should also enrol in Quantum Mechanics II, and Quantum Mechanics III next semester. The next level up -- Year 4 or graduate level -- is Condensed Matter Field Theory, which has as its starting point the first half of QMIII, parts of QMII, and all of SPIII.
• Assignment exercises are an essential part of both the lecture course and the associated tutorial course. Students are thereby given the opportunity to master a wide variety of technical variations encountered in solving concrete examples of the basic theory, as well as a variety of simple extensions of the basic theory, and simple analytical techniques related to the lecture content but not mentioned in lectures, that extend the scope of problems that students can solve. Mastery of these aspects makes students more attractive to employers or future research labs.
• Reliance on generative AI is harmful to learning. In view of the importance of mastering the assignment exercises, measures will be put in place to ensure that students who rely on generative AI and group discussions to attain high assignment scores without mastering the content will not achieve an inflated final grade: inflated grades are unfair to students who take learning seriously and achieve scholarly independence by genuine hard work. After completing this course, if they do use AI to help with study, students should aim to be capable of critiquing the output from generative AI and evaluating its correctness -- in other words, they must be capable of solving the exercises as well as creative variations of the exercises and lecture content with no help. No employer wants employees who are unable to do this.
Face-to-face lectures, tutorials and exams are opportunities to demonstrate your scholarly independence. Even students who find mathematics difficult can attain at least a "B" grade by engaging in good study habits and learning the lecture content well, and by being honest with themselves and others.
All lecture course exams (Midsemester, End of Semester) are in-class. Students who rely on generative AI and do not master the content will reveal themselves when they are unable to make any meaningful progress with exam questions that are simple repetitions of assignment questions with minor changes or simple creative variations. Even students who have difficulty with mathematics can master such questions if they have good study habits and perseverence. Excessive reliance on generative AI and group discussions wastes time, harms your learning and affects your grades. |
| | 授業開講形態等
Lecture format, etc. | | Lecture format, etc.
Face to face lectures and tutorials are compulsory (other than in exceptional circumstances; e.g. COVID infection). However, in order to record a video of the lecture -- including student interaction with each other and with the lecturer -- the lectures will simultaneously be carried out online using MS Teams. Students are therefore requested to bring their laptop or tablet to the lecture room. Make sure it has a microphone. Bring an electrical cord. For many G30 students, English is a 2nd or even 3rd language, so video recordings are an invaluable learning aid.
Live lectures via MS Teams (face-to-face and online). Before the start of semester students should ensure that they have correctly installed MS Teams using their THERS (国立大学法人東海国立大学機構 ) email account.
NUPACE students should contact Professor John Wojdylo before the start of semester for assistance with installing Teams correctly. |
| | 遠隔授業（オンデマンド型）で行う場合の追加措置
Additional measures for remote class (on-demand class) | | Additional measures for remote class (on-demand class)
All lectures will be live face-to-face and online via MS Teams. Face-to-face attendance is compulsory (barring exceptional circumstances such as COVID infection).
A lecture video will be available immediately after each lecture to help with student revision.
The lecturer will be available to answer questions via Teams chat. |
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