# MS\&E 460: Introduction to Computational Materials Science and Engineering | UW–Madison [View on UW Courses](https://uwcourses.com/courses/MS%26E_460) ## Dataset ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ### repository twangodev/uwcourses ### schema version 6 ### importer version 7 ### projection id 94468b7d4154126ca413937bb4738a6d21e0c2277f6fa397a6078ad80a32384f ### observed at 2026-09-07T15:55:43.033547+00:00 ### built at 2026-09-10T23:19:01.870283+00:00 ### courses 8951 ### current instructors 5754 ### limited false ### terms * 1272 * 1264 * 1262 * 1254 * 1252 * 1244 * 1242 * 1234 * 1232 * 1224 * 1222 * 1212 * 1204 * 1202 * 1194 * 1192 * 1184 * 1182 * 1174 * 1172 * 1164 * 1162 * 1154 * 1152 * 1144 * 1142 * 1134 * 1132 * 1124 * 1122 * 1114 * 1112 * 1104 * 1102 * 1094 * 1092 * 1084 * 1082 * 1074 * 1072 ### term 1272 ### departments | subject | count | | --------- | ----- | | AAE | 90 | | ABT | 20 | | ACCTIS | 36 | | ACTSCI | 14 | | AFAERO | 10 | | AFRICAN | 87 | | AFROAMER | 71 | | AGROECOL | 21 | | AMERIND | 53 | | ANAT\&PHY | 7 | | ANATOMY | 2 | | ANESTHES | 8 | | ANSCI | 57 | | ANTHRO | 95 | | ART | 125 | | ARTED | 10 | | ARTHIST | 121 | | ASIALANG | 129 | | ASIAN | 109 | | ASIANAM | 28 | | ASTRON | 38 | | ATMOCN | 69 | | BIOCHEM | 51 | | BIOCORE | 10 | | BIOLOGY | 14 | | BIOMDSCI | 18 | | BME | 66 | | BMI | 41 | | BMOLCHEM | 10 | | BOTANY | 69 | | BSE | 44 | | C\&ESOC | 73 | | CBE | 48 | | CHEM | 101 | | CHICLA | 52 | | CIVENGR | 133 | | CLASSICS | 47 | | CNP | 10 | | CNSRSCI | 50 | | COMARTS | 137 | | COMPBIO | 15 | | COMPLIT | 11 | | COMPSCI | 138 | | COUNPSY | 78 | | CRB | 19 | | CS\&D | 71 | | CSCS | 40 | | CURRIC | 193 | | DANCE | 93 | | DERM | 8 | | DS | 88 | | DYSCI | 35 | | ECE | 155 | | ECON | 145 | | EDPOL | 114 | | EDPSYCH | 97 | | ELPA | 70 | | EMA | 53 | | EMERMED | 17 | | ENGL | 206 | | ENTOM | 40 | | ENVIRST | 148 | | EP | 15 | | EPD | 67 | | ESL | 16 | | F\&WECOL | 60 | | FAMMED | 22 | | FINANCE | 50 | | FOLKLORE | 40 | | FOODSCI | 43 | | FRENCH | 63 | | GEN\&WS | 145 | | GENBUS | 72 | | GENECSLR | 17 | | GENETICS | 60 | | GEOG | 113 | | GEOSCI | 83 | | GERMAN | 82 | | GLE | 48 | | GNS | 38 | | GREEK | 27 | | HDFS | 41 | | HEBR-BIB | 13 | | HEBR-MOD | 10 | | HISTORY | 233 | | HISTSCI | 56 | | HONCOL | 13 | | ILS | 43 | | INFOSYS | 9 | | INTEGART | 7 | | INTEGSCI | 22 | | INTER-AG | 18 | | INTER-HE | 11 | | INTER-LS | 22 | | INTEREGR | 16 | | INTLBUS | 21 | | INTLST | 50 | | ISYE | 83 | | ITALIAN | 53 | | JEWISH | 56 | | JOURN | 88 | | KINES | 128 | | LACIS | 26 | | LANDARC | 61 | | LATIN | 24 | | LAW | 120 | | LEGALST | 48 | | LINGUIS | 35 | | LIS | 89 | | LITTRANS | 78 | | LSC | 52 | | M\&ENVTOX | 7 | | MARKETNG | 62 | | MATH | 153 | | MDGENET | 8 | | ME | 130 | | MEDHIST | 43 | | MEDICINE | 60 | | MEDIEVAL | 32 | | MEDPHYS | 39 | | MEDSC-M | 29 | | MEDSC-V | 41 | | MHR | 68 | | MICROBIO | 45 | | MILSCI | 16 | | MM\&I | 22 | | MOLBIOL | 6 | | MS\&E | 59 | | MUSIC | 165 | | MUSPERF | 126 | | NAVSCI | 20 | | NE | 44 | | NEURODPT | 11 | | NEUROL | 7 | | NEURSURG | 4 | | NTP | 11 | | NURSING | 111 | | NUTRSCI | 65 | | OBS\&GYN | 18 | | OCCTHER | 39 | | ONCOLOGY | 14 | | OPHTHALM | 5 | | OTM | 43 | | PATH | 34 | | PATH-BIO | 29 | | PEDIAT | 26 | | PHARMACY | 31 | | PHILOS | 77 | | PHMCOL-M | 11 | | PHMPRAC | 45 | | PHMSCI | 60 | | PHYASST | 37 | | PHYSICS | 88 | | PHYSIOL | 3 | | PHYTHER | 37 | | PLANTSCI | 52 | | PLPATH | 35 | | POLISCI | 192 | | POPHLTH | 58 | | PORTUG | 33 | | PSYCH | 101 | | PSYCHIAT | 22 | | PUBAFFR | 54 | | PUBLHLTH | 23 | | RADIOL | 11 | | REALEST | 41 | | RELIGST | 90 | | RHABMED | 9 | | RMI | 24 | | RP\&SE | 102 | | S\&APHM | 17 | | SCANDST | 73 | | SLAVIC | 89 | | SOC | 149 | | SOCWORK | 87 | | SOILSCI | 46 | | SPANISH | 83 | | SRMED | 21 | | STAT | 94 | | STDYABRD | 52 | | STS | 8 | | SURGERY | 25 | | SURGSCI | 33 | | THEATRE | 91 | | URBRPL | 60 | | UROLOGY | 5 | | ZOOLOGY | 89 | ## course ### run id 20260907T155543-ce3781c4 ### semester 1272 ### observed at 2026-09-07 15:55:43.033547+00:00 ### record version id e35d43bbdac07fc212aa8a9cb5e80099c85e85b70ea02fc5d69e87dbfdd71fa8 ### course id MS\&E 460 ### course uid course\_3328fa9825020bd75916726c ### catalog version id 979d3852c7d34081776d32980ea09f0af5e6793403866cf3e557ac655bbba589 ### course number 460 ### subjects * MS\&E ### title INTRODUCTION TO COMPUTATIONAL MATERIALS SCIENCE AND ENGINEERING ### description An introduction to the theoretical and computational tools for computational materials, with hands on homework and laboratories. Topics may include atomistic simulation (e.g., molecular dynamics), mesoscale simulation (e.g,. Phase field method), macroscale simulation (e.g., finite element method), thermodynamic and kinetic modeling (CALPHAD method), informatics (e.g., machine learning), and special topics (e.g., solar cell design, electronic device simulation, etc.) ### requirements text None ### credit offering ids None recorded. ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id f32bc48bfcd3de01edef41b4811a9f3561d27b5a7e146771621ecc4a7fd2d5e3 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary Introduction to theoretical and computational tools for materials science, covering atomistic, mesoscale, and macroscale simulations. ### llm topics * Atomistic simulation and molecular dynamics * Mesoscale simulation and phase field methods * Macroscale simulation and finite element methods * Thermodynamic and kinetic modeling (CALPHAD) * Materials informatics and machine learning * Special topics in materials design and simulation ### llm skills * Practical application of computational tools through labs and homework * Performing atomistic simulations * Conducting mesoscale simulations * Executing macroscale simulations * Modeling thermodynamics and kinetics * Applying informatics and machine learning to materials ### llm assumed background None recorded. ### llm search phrases * computational materials science * atomistic simulation molecular dynamics * mesoscale simulation phase field * finite element method FEM * CALPHAD thermodynamic modeling * materials informatics machine learning * solar cell design simulation ### llm requirements status valid ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash 868f2986c9c770924127a805b8c8001ab40ac6ad663d7c6e162c257f0b6df44d #### course id MS\&E 460 #### current instructors None recorded. #### difficulty workload None recorded. #### errors None recorded. #### historical context None recorded. #### message No course-specific reviews available #### offered false #### profile hash 5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02 #### quick take Recent recorded grades — Spring 2024: 3.83 GPA, 91.4% A/AB (n=35 letter grades); Spring 2025: 3.62 GPA, 82.1% A/AB (n=28 letter grades); Spring 2026: 3.84 GPA, 97.1% A/AB (n=34 letter grades). ```json { "citations": [ { "course_id": "MS&E 460", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "2b560645-880a-3825-b62f-cbfa9e01d713", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1244", "type": "grade" }, { "course_id": "MS&E 460", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "2b560645-880a-3825-b62f-cbfa9e01d713", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1254", "type": "grade" }, { "course_id": "MS&E 460", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "2b560645-880a-3825-b62f-cbfa9e01d713", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1264", "type": "grade" } ] } ``` #### student experience None recorded. #### task hash 74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68 #### teaching history None recorded. #### term id 1272 #### term name 2026 Fall #### version 2 ### requirements #### nodes ```json [ { "children": [], "condition": "None", "course": null, "evidence": "None", "id": "source_requirements", "kind": "condition" } ] ``` #### notes * Display fallback using original requirements text; not an LLM-parsed rule. #### root source\_requirements #### status needs\_review ### instructors None recorded. ### offerings None recorded. ### sections None recorded. ### grade instructors ```json [ { "instructor_uid": "instructor_d5bed9e0e3e7e6e10ac83b63", "source": "madgrades", "source_instructor_id": "3949360", "identity_basis": "source_id", "identity_status": "source_identified", "name": "DANE MORGAN", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "grade_statistics": { "gpa": 3.823, "graded": 1136, "counts": [ 885, 149, 72, 22, 5, 0, 3 ], "sections": 196 }, "instructor_url": "/instructors/DANE_MORGAN--instructor_d5bed9e0e3e7e6e10ac83b63" }, { "instructor_uid": "instructor_6bd5cd6ffc733d5b95b5c91f", "source": "madgrades", "source_instructor_id": "4294595", "identity_basis": "source_id", "identity_status": "source_identified", "name": "YUAN PING", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "grade_statistics": { "gpa": 3.736, "graded": 127, "counts": [ 71, 46, 9, 1, 0, 0, 0 ], "sections": 28 }, "instructor_url": "/instructors/YUAN_PING--instructor_6bd5cd6ffc733d5b95b5c91f" }, { "instructor_uid": "instructor_ffc5938ab6a062faeb8475a9", "source": "madgrades", "source_instructor_id": "6079238", "identity_basis": "source_id", "identity_status": "source_identified", "name": "MINYI DAI", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "grade_statistics": { "gpa": 3.829, "graded": 35, "counts": [ 26, 6, 3, 0, 0, 0, 0 ], "sections": 1 }, "instructor_url": "/instructors/MINYI_DAI--instructor_ffc5938ab6a062faeb8475a9" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "MS&E 460", "course_uid": "course_3328fa9825020bd75916726c", "term_id": "1224", "term_name": "Spring 2022", "instructors": [ "Dane Morgan" ], "a": 27, "ab": 6, "b": 2, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 2, "no_work": 0, "not_reported": 0, "other": 1, "total": 38, "source_aliases": [ "MS&E 460" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "MS&E 460", "course_uid": "course_3328fa9825020bd75916726c", "term_id": "1234", "term_name": "Spring 2023", "instructors": [ "Dane Morgan" ], "a": 37, "ab": 17, "b": 3, "bc": 1, "c": 0, "d": 0, "f": 1, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 1, "no_work": 0, "not_reported": 0, "other": 0, "total": 60, "source_aliases": [ "MS&E 460" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "MS&E 460", "course_uid": "course_3328fa9825020bd75916726c", "term_id": "1244", "term_name": "Spring 2024", "instructors": [ "MINYI DAI", "Yuan Ping" ], "a": 26, "ab": 6, "b": 3, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 35, "source_aliases": [ "MS&E 460" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "MS&E 460", "course_uid": "course_3328fa9825020bd75916726c", "term_id": "1254", "term_name": "Spring 2025", "instructors": [ "Yuan Ping" ], "a": 13, "ab": 10, "b": 4, "bc": 1, "c": 0, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 28, "source_aliases": [ "MS&E 460" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "MS&E 460", "course_uid": "course_3328fa9825020bd75916726c", "term_id": "1264", "term_name": "Spring 2026", "instructors": [ "Yuan Ping" ], "a": 24, "ab": 9, "b": 1, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 34, "source_aliases": [ "MS&E 460" ] } ] ``` ### statistics #### gpa 3.77 #### graded 191 #### counts * 127 * 48 * 13 * 2 * 0 * 0 * 1 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_3328fa9825020bd75916726c-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_3328fa9825020bd75916726c-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_3328fa9825020bd75916726c-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_3328fa9825020bd75916726c-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_3328fa9825020bd75916726c-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_3328fa9825020bd75916726c-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.769633507853403 #### count 191 #### university ##### size 2947 ##### gpa Percentile 53 ##### count Percentile 67 ##### median Count 113 ##### histogram | range | count | current | | ------- | ----- | ------- | | 0.0–0.4 | 0 | false | | 0.4–0.8 | 0 | false | | 0.8–1.2 | 0 | false | | 1.2–1.6 | 0 | false | | 1.6–2.0 | 0 | false | | 2.0–2.4 | 1 | false | | 2.4–2.8 | 11 | false | | 2.8–3.2 | 200 | false | | 3.2–3.6 | 728 | false | | 3.6–4.0 | 2007 | true | #### departments ```json [ { "subject": "MS&E", "comparison": { "size": 20, "gpaPercentile": 74, "countPercentile": 89, "medianCount": 107, "histogram": [ { "range": "0.0–0.4", "count": 0, "current": false }, { "range": "0.4–0.8", "count": 0, "current": false }, { "range": "0.8–1.2", "count": 0, "current": false }, { "range": "1.2–1.6", "count": 0, "current": false }, { "range": "1.6–2.0", "count": 0, "current": false }, { "range": "2.0–2.4", "count": 0, "current": false }, { "range": "2.4–2.8", "count": 0, "current": false }, { "range": "2.8–3.2", "count": 3, "current": false }, { "range": "3.2–3.6", "count": 7, "current": false }, { "range": "3.6–4.0", "count": 10, "current": true } ] } } ] ``` ### terms #### 1224 ##### term 1224 ##### gpa 3.857142857142857 ##### count 35 ##### university ###### size 1171 ###### gpa Percentile 77 ###### count Percentile 13 ###### median Count 64 ###### histogram | range | count | current | | ------- | ----- | ------- | | 0.0–0.4 | 0 | false | | 0.4–0.8 | 0 | false | | 0.8–1.2 | 0 | false | | 1.2–1.6 | 0 | false | | 1.6–2.0 | 0 | false | | 2.0–2.4 | 3 | false | | 2.4–2.8 | 21 | false | | 2.8–3.2 | 173 | false | | 3.2–3.6 | 358 | false | | 3.6–4.0 | 616 | true | ##### departments ```json [ { "subject": "MS&E", "comparison": { "size": 10, "gpaPercentile": 67, "countPercentile": 33, "medianCount": 38, "histogram": [ { "range": "0.0–0.4", "count": 0, "current": false }, { "range": "0.4–0.8", "count": 0, "current": false }, { "range": "0.8–1.2", "count": 0, "current": false }, { "range": "1.2–1.6", "count": 0, "current": false }, { "range": "1.6–2.0", "count": 0, "current": false }, { "range": "2.0–2.4", "count": 0, "current": false }, { "range": "2.4–2.8", "count": 0, "current": false }, { "range": "2.8–3.2", "count": 2, "current": false }, { "range": "3.2–3.6", "count": 3, "current": false }, { "range": "3.6–4.0", "count": 5, "current": true } ] } } ] ``` #### 1234 ##### term 1234 ##### gpa 3.711864406779661 ##### count 59 ##### university ###### size 1188 ###### gpa Percentile 58 ###### count Percentile 44 ###### median Count 65 ###### histogram | range | count | current | | ------- | ----- | ------- | | 0.0–0.4 | 0 | false | | 0.4–0.8 | 0 | false | | 0.8–1.2 | 0 | false | | 1.2–1.6 | 0 | false | | 1.6–2.0 | 0 | false | | 2.0–2.4 | 3 | false | | 2.4–2.8 | 17 | false | | 2.8–3.2 | 144 | false | | 3.2–3.6 | 374 | false | | 3.6–4.0 | 650 | true | ##### departments | subject | comparison | | ------- | ---------- | | MS\&E | | #### 1244 ##### term 1244 ##### gpa 3.8285714285714287 ##### count 35 ##### university ###### size 1241 ###### gpa Percentile 71 ###### count Percentile 11 ###### median Count 65 ###### histogram | range | count | current | | ------- | ----- | ------- | | 0.0–0.4 | 0 | false | | 0.4–0.8 | 0 | false | | 0.8–1.2 | 0 | false | | 1.2–1.6 | 0 | false | | 1.6–2.0 | 0 | false | | 2.0–2.4 | 2 | false | | 2.4–2.8 | 9 | false | | 2.8–3.2 | 151 | false | | 3.2–3.6 | 373 | false | | 3.6–4.0 | 706 | true | ##### departments | subject | comparison | | ------- | ---------- | | MS\&E | | #### 1254 ##### term 1254 ##### gpa 3.625 ##### count 28 ##### departments | subject | comparison | | ------- | ---------- | | MS\&E | | #### 1264 ##### term 1264 ##### gpa 3.838235294117647 ##### count 34 ##### university ###### size 1283 ###### gpa Percentile 69 ###### count Percentile 8 ###### median Count 66 ###### histogram | range | count | current | | ------- | ----- | ------- | | 0.0–0.4 | 0 | false | | 0.4–0.8 | 0 | false | | 0.8–1.2 | 0 | false | | 1.2–1.6 | 0 | false | | 1.6–2.0 | 0 | false | | 2.0–2.4 | 1 | false | | 2.4–2.8 | 20 | false | | 2.8–3.2 | 130 | false | | 3.2–3.6 | 343 | false | | 3.6–4.0 | 789 | true | ##### departments | subject | comparison | | ------- | ---------- | | MS\&E | | ### benchmarks #### all ##### school ###### size 2947 ###### gpa 3.684503227431485 ###### top Share 83.48969667841149 ###### count 113 ##### MS\&E ###### size 20 ###### gpa 3.575450114987998 ###### top Share 75.73562262342936 ###### count 107 #### terms ##### 1224 ###### school ###### size 1171 ###### gpa 3.5627882428500937 ###### top Share 76.37271640560496 ###### count 64 ###### MS\&E ###### size 10 ###### gpa 3.576880199074966 ###### top Share 76.2136436133529 ###### count 38 ##### 1234 ###### school ###### size 1188 ###### gpa 3.5799441677552393 ###### top Share 77.18256448537606 ###### count 65 ##### 1244 ###### school ###### size 1241 ###### gpa 3.5975573126929192 ###### top Share 78.29036874847135 ###### count 65 ##### 1254 ###### school ###### size 1289 ###### gpa 3.6126423469389106 ###### top Share 79.48050408754871 ###### count 66 ##### 1264 ###### school ###### size 1283 ###### gpa 3.6229729166451925 ###### top Share 80.13669149396227 ###### count 66 ## instructor Trends ```json [ { "uid": "instructor_6bd5cd6ffc733d5b95b5c91f", "name": "YUAN PING", "count": 97, "terms": [ { "term": "1244", "count": 35, "sections": 1, "gpa": 3.8285714285714287 }, { "term": "1254", "count": 28, "sections": 1, "gpa": 3.625 }, { "term": "1264", "count": 34, "sections": 1, "gpa": 3.838235294117647 } ] }, { "uid": "instructor_d5bed9e0e3e7e6e10ac83b63", "name": "DANE MORGAN", "count": 94, "terms": [ { "term": "1224", "count": 35, "sections": 1, "gpa": 3.857142857142857 }, { "term": "1234", "count": 59, "sections": 1, "gpa": 3.711864406779661 } ] }, { "uid": "instructor_ffc5938ab6a062faeb8475a9", "name": "MINYI DAI", "count": 35, "terms": [ { "term": "1244", "count": 35, "sections": 1, "gpa": 3.8285714285714287 } ] } ] ``` ## following None recorded.