Major Course Study Roadmap

Basis: the Cognitive Science and Technology (Language Learning Science Experimental Class) Program of Study
Beijing Language and Culture University · School of Education · First cohort, 2026 · Bachelor of Education · 149 credits

1 What this document is, and what it is not

⚠️ Honest disclosure: I do not have the syllabus or the textbook for a single one of these courses.
The description below of what each course "covers" is written from the course title + the standard content of that discipline.
It does not necessarily equal what your teacher actually teaches. Once you get a syllabus or have taken the class, tell me, and I will realign and correct it.

2 What your major is

It is not "cognitive science + a bit of education". It is:

             Education (the degree spine)
                    │
        ┌───────────┼───────────┐
        │           │           │
Cognitive science ──┼─────  Linguistics
        │           │           │
        └────  Computing / AI  ────┘
          (the third leg, running through)

How to read it:

• Education is your degree (Bachelor of Education) and also the heaviest block in class hours
• Cognitive science and linguistics are the two pillars, holding up the question "how do humans learn language"
• Computing / AI is the cross-cutting tool — every course marked ↗ in your program is taught in the same classroom as the Artificial Intelligence major in the School of Information Science

In one sentence: the question you have to answer is "how do humans learn language, and can a machine reproduce it".

Two resources to squeeze dry:

1. An academic advisor for every student — everyone is assigned an academic advisor and can join research projects. Your need for "research is mandatory for a master's application" can start in Year 1
2. The ↗ shared courses — you can take deep learning / NLP / large-model courses alongside the Artificial Intelligence and Language Science majors. No other cognitive science program has this

3 How the 149 credits break down

💡 Note the "46 credits of major compulsory courses" — this is your real main battlefield.
Of the 24 general-education elective credits, 6 must be aesthetic education (美育) and 2 must be the "四史" (the "Four Histories": Party history, New China history, reform-and-opening-up history, and socialist development history). Do not leave a gap when you pick courses.

4 Master table of compulsory major courses (by semester)

23 courses, 46 credits.

⚠️ Two key findings:

① Year 2, Spring (Semester 4) is the heaviest semester, with 12 credits and 6 courses at once.
That semester also stacks the major practicum on top. Knowing this in advance matters — you should be mentally prepared by the winter break at the end of Year 2, Fall.

② After Year 3, Spring the compulsory major courses are almost empty (only 2 credits left).
The time is left for internship, graduation thesis, research, and applications. So Year 3, Spring is your golden window for research output —
master's applications are submitted in Fall 2029 (Year 4, Fall), so your results need to be basically in shape before Year 3, Spring ends.

* = carries an innovation-and-entrepreneurship marker　↗ = a shared course with the Artificial Intelligence / Language Science majors

5 Semester-by-semester detail

5.1 Semester 1 · Year 1, Fall (where you are now)

The focus of this semester:

• All four are outline / introductory courses; what is tested is "building a framework", not "digging into detail"
• General Psychology and Introduction to Linguistics are the foundation of every course that follows — get these two solid and Year 2 will be much easier
• Do not underestimate Advanced Mathematics: the program gives it only 2 credits, but the cognitive science master's track in your plan needs stronger mathematics.
  → Beyond this on-campus course, add your own workload following the 16 stages in the Study Roadmap

Companion notes:
- General Psychology
- Introduction to Linguistics
- Principles of Education
- Mathematics → see Study Roadmap / Stages

5.2 Semester 2 · Year 1, Spring

The focus of this semester:

• Introduction to Cognitive Science is the most important one — it is your major's "identity card"
• Chapter 1 of Simon's Cognition maps directly onto this course (see section 7)
• Modern Chinese looks like a "Chinese class", but it is really about making you see linguistic structure in your own native language — a prerequisite for running language experiments

5.3 Semester 3 · Year 2, Fall

This is the semester where "cognitive science + AI" starts to hit its stride. You will touch the brain (neuroscience) and the machine (AI) at the same time — this is exactly the core tension of cognitive science.

💡 At the end of Semester 3 there is a major seminar (Year 3, Fall, 1 credit).

5.4 Semester 4 · Year 2, Spring ⚠️ the heaviest semester

6 courses, 12 credits, plus the major practicum at the same time.

⚠️ This semester is going to be rough. 6 courses + practicum.
Suggestion: start previewing Cognitive Psychology and Research Methods in Education during the winter break of Year 2, Fall — these two are the key to your research track.

5.5 Semester 5 · Year 3, Fall

💡 Educational Statistics is the key course this semester — it is the foundation of all your later empirical research,
and it complements the mathematical statistics in the Mathematics module (the on-campus course leans applied, self-study leans theoretical).

5.6 Semesters 6–8 · Year 3, Spring to Year 4

Only one compulsory major course is left: "Curriculum and Instruction Theory" (Semester 6, 2 credits).

The remaining time goes to:

⚠️ Social practice requires more than 30 days, and proof must be submitted. Do not wait until Year 3 to remember it.

6 The four threads

Group the 23 compulsory courses by discipline and your own knowledge structure becomes clear:

6.1 The education thread (9 courses, 18 credits) — your degree spine

Principles of Education → Educational Psychology → Curriculum and Instruction Theory → Philosophy of Education → History of Chinese and Foreign Education →
Educational Assessment (I & II) → Educational Statistics → Research Methods in Education → Language Pedagogy → Language Teaching Assessment and Application

6.2 The cognitive science thread (5 courses, 10 credits)

General Psychology → Introduction to Cognitive Science → Cognitive Psychology → Cognitive Neuroscience of Language → Introduction to the Learning Sciences

6.3 The linguistics thread (4 courses, 8 credits)

Introduction to Linguistics → Modern Chinese → Introduction to Cognitive Linguistics → Introduction to Second Language Acquisition

6.4 The computing / AI thread (2 compulsory courses, 4 credits) + electives

Principles of Artificial Intelligence ↗ → Principles and Design of Intelligent Agents ↗
(+ from the electives: Deep Learning: Principles and Practice / Natural Language Processing / Large Language Models / Introduction to Language Engineering / Computational Language Structure / Quantitative Linguistics)

⭐ See it?: the computing thread has only 4 credits of compulsory courses — everything else depends on electives.
And given your goal of a "cognitive science master's", computational ability is precisely what sets you apart from the pack.
So with your 16 elective credits, put the bulk into the computing thread (see the next section).

7 How to choose your 16 elective credits

Rule: of the three modules (education / cognitive science / linguistics) you must choose at least two, with no fewer than 6 credits in each module, for 16 credits in total.

7.1 Plan A (recommended): cognitive science + linguistics

Why: it directly strengthens your "two legs", and the computing courses are concentrated in the linguistics module.

(cognitive science 6 + linguistics 10 = 16 ✓)

7.2 Plan B: education + linguistics

Why: if your research interest leans towards language teaching / educational assessment (the School of Education's own specialty).

7.3 Three principles for picking courses

1. Prioritise the ↗ shared courses — they are taught in the same classroom as the Artificial Intelligence major, with better faculty and higher difficulty, so they are a chance to "piggyback on good resources"
2. Methods / statistics courses are a must — Educational Statistics, Statistical Methods for Language Teaching Research, Questionnaire Design and Analysis. These are your research tools, and are worth more than one extra theory course
3. Choosing a Research Topic and Academic Writing (Semester 7) is worth taking — it directly serves your graduation thesis and your master's application writing

⚠️ Watch which semester a course runs in: many good courses cluster in Semester 5 (Year 3, Fall), and that semester gets crowded. Plan ahead.

8 How this connects to your overall plan

⭐ The single biggest mismatch: your major carries very little maths and programming (2 credits of calculus, no compulsory programming),
yet cognitive science master's programs assume you are good at both maths and programming.
This gap has to be filled by self-study — and that is exactly the value of the 16-stage study roadmap you are already following.

9 Bibliography

9.1 Confirmed reference books

9.2 Free online resources

See the Major Course Textbook Index.

10 Notes index

21 of the 23 compulsory major courses have notes. (Cognitive Psychology and Research Methods in Education do not, for now.)

10.1 Semester 1 (Year 1, Fall)

10.2 Semester 2 (Year 1, Spring)

10.3 Semester 3 (Year 2, Fall)

10.4 Semester 4 (Year 2, Spring, the heaviest semester)

10.5 Semester 5 (Year 3, Fall)

10.6 Semester 6 (Year 3, Spring)

10.7 Reference books

⚠️ Every set of notes carries the same caveat: I have not obtained the syllabus,
so the content is organised around the standard disciplinary framework — your teacher's syllabus and lectures take precedence.

11 Benchmarks for checking yourself

By the end of Year 1, Fall:

1. You can explain clearly that your major is a three-part structure of education + cognitive science + linguistics, and how the three relate
2. You can draw the frameworks of General Psychology and Introduction to Linguistics yourself
3. You have met at least one academic advisor and know their research direction

By the end of Year 2, Fall:

1. You can read an empirical paper in cognitive science / linguistics (methods, results, conclusions)
2. You know what the N400 and P600 components of an ERP correspond to
3. You have started doing a bit of work with your advisor (even if only organising literature)

By the end of Year 3, Spring:

1. You have one project you can write into an application
2. You can do statistical analysis independently
3. Your graduation thesis has a preliminary direction

12 Related

• Major Course Textbook Index
• University Plan
• Study Roadmap — the 16 stages for maths / programming (filling the maths-and-programming gap in the major)
• English Vocabulary