IEP Goal AreaExpressive languageASHA School-Based

Expressive Language IEP Goals

SMART expressive language IEP goal templates, LSA-informed baselines, and progress-monitoring cadence for school-based SLPs targeting morphosyntax and sentence production.

Overview

Expressive language goals cover everything a student produces: sentence length, grammatical morphology, narrative structure, word-finding, and formulated discourse. On most school caseloads this is the second-largest goal area after articulation, and it is the area where the defensibility of the baseline matters most — unlike articulation, where a probe gives you a number immediately, expressive language requires a sampled measure that can vary five points between sessions if the sampling protocol is loose. IDEA requires that the goal be measurable and tied to a documented present level, and for expressive language that almost always means a language sample metric (MLU-M, NDW, PGU, or a morpheme percentage) computed on a fixed-length sample and reported verbatim in the present levels statement. This is the single goal area where ConductSpeech pays for itself the fastest, because every minute of transcription time the clinician saves is a minute that goes back into therapy.

Every IEP goal on this page is written in the SMART format required by IDEA 34 CFR §300.320(a)(2) — Specific, Measurable, Achievable, Relevant, Time-bound. Clinicians must adapt templates to the individual student's baseline, classroom context, and state-level IDEA implementation regulations before dropping them into an IEP.

Baseline measurement protocol

Every defensible expressive language IEP goal starts with a documented baseline. Follow this protocol before you open the goal generator.

  1. Collect a 50-utterance language sample using a consistent elicitation protocol (free play, picture description, narrative retell, or conversation).
  2. Transcribe to SALT or an equivalent convention. If using ConductSpeech, upload the audio and the metrics generate automatically.
  3. Compute MLU-M, NDW-50, TTR, and PGU on the sample.
  4. Report the baseline in the present levels statement verbatim: "On a 50-utterance free-play sample, MLU-M = X.X, NDW-50 = Y, PGU = Z%."
  5. Compare against the age-matched SUGAR or LDS norms and flag any measure more than one standard deviation below the mean.
  6. Note the elicitation context explicitly so the mid-year progress sample matches.

How language sample analysis informs expressive language goals

Language sample analysis is the foundation of every defensible expressive language IEP goal. A 50-utterance sample from structured play, picture description, or narrative retell produces MLU-M, NDW, type-token ratio, PGU, and Brown's morpheme percentages in a single pass — and each of those numbers can become the baseline for a separate goal or a supporting data point in a single comprehensive goal. For a 6-year-old with a CELF-5 Core in the average range but obvious sentence-length limits in classroom discourse, the LSA is the instrument that documents the problem. For an 8-year-old with a depressed Recalling Sentences score, the LSA is the instrument that tells you whether the deficit shows up in connected speech too. Write the goal off the LSA, collect mid-year progress data with a matched 50-utterance sample, and the annual review will write itself.

Every expressive language goal on every IEP should be anchored to a number from a language sample. If you cannot say "the baseline MLU-M is X.X from a 50-utterance sample" you are writing the goal from guesses.
Anchor the goal to a sampled number

SMART expressive language IEP goal templates

Five ready-to-paste templates. Replace the bracketed placeholders with the student's name, the annual review date, and your target number from the baseline protocol above.

1

Increase MLU-M in connected speech

By {annual review date}, on a 50-utterance free-play language sample elicited by the SLP, {Student} will produce a mean length of utterance in morphemes of at least {target MLU}, measured on two consecutive samples across a 6-week window.

Typical baseline
2.5-4.0 MLU-M at baseline
Typical annual target
4.0-5.5 MLU-M at annual review
2

Increase lexical diversity (NDW-50)

By {annual review date}, on a 50-utterance narrative retell, {Student} will produce at least {target NDW} different words, measured on two consecutive samples across a 6-week window.

Typical baseline
60-95 NDW-50 at baseline
Typical annual target
110-140 NDW-50 at annual review
3

Produce regular past tense -ed in obligatory contexts

By {annual review date}, during a 50-utterance language sample, {Student} will produce regular past tense -ed in at least 85% of obligatory contexts across two consecutive samples as measured by the SLP.

Typical baseline
30-60% in obligatory contexts
Typical annual target
85% in obligatory contexts
4

Formulate compound and complex sentences

By {annual review date}, given a picture prompt and a sentence-starter scaffold, {Student} will formulate grammatically correct compound or complex sentences in 8 of 10 trials (80%) across three consecutive probe sessions as measured by the SLP.

Typical baseline
2-4 of 10 correct (20-40%)
Typical annual target
8 of 10 correct (80%)
5

Retell a short story with required narrative elements

By {annual review date}, after listening to a grade-level 6-8 sentence story, {Student} will retell the story including at least 5 of 6 required narrative elements (character, setting, initiating event, plan, action, consequence) in 4 of 5 trials as measured by SLP rubric scoring.

Typical baseline
2-3 of 6 elements per retell
Typical annual target
5-6 of 6 elements per retell

Progress monitoring cadence

  1. Collect a matched 25-50 utterance progress sample every 6-8 weeks using the same elicitation context as the baseline.
  2. Recompute the target metric and plot on a progress chart in the IEP data system.
  3. When the student hits the annual target, advance to a more complex elicitation context (e.g., from free play to narrative retell).
  4. If progress is flat across two consecutive samples, narrow the goal to a single specific morpheme or sentence type and re-probe.
  5. Summarise baseline, mid-year, and end-of-year sample metrics in the annual review present levels section.

Common pitfalls in expressive language IEP goals

  • Using a standardised-test scaled score as a goal target ("increase CELF-5 Formulated Sentences to 10") — standardised scores are not a legal IEP goal, they are a data point.
  • Setting a vague goal like "increase sentence length" without a measurable metric — the annual review will have nothing to score against.
  • Sampling in a different elicitation context at mid-year — progress data only means something when baseline and progress samples match.
  • Writing five morpheme goals on a single IEP — pick the two or three most functionally important morphemes and build the others into therapy.
  • Ignoring the comprehension side of the profile — expressive goals must be interpreted against receptive language status.

Free tools for expressive language IEP work

IEP Goal Generator

Free interactive IEP (Individualised Education Programme) goal generator for school-based speech-language pathologists, special-education teachers, and IEP teams. Pick the goal area (one of the eight ASHA School-Based Service Delivery areas: articulation, expressive language, receptive language, fluency, voice, pragmatics / social communication, AAC, literacy), pick the target skill from the curated bank of 30+ starter skills, enter the baseline percent and the target percent, set the consecutive-sessions mastery criterion and the annual-review deadline, and the tool drafts a SMART (Specific, Measurable, Achievable, Relevant, Time-bound) IEP goal sentence ready to paste into the IEP. Includes a SMART self-check rubric, a customisable condition clause, a copy-to-clipboard button, and suggested baseline / target ranges that match published school-age SLP intervention practice. Mobile-friendly, client-side, no sign-up.

Open tool

MLU Calculator

Paste a language sample and get Mean Length of Utterance in morphemes and words, total utterances, total morphemes, and the matching Brown's stage. Implements Brown (1973) morpheme counting rules and runs entirely in your browser.

Open tool

Lexical Diversity Calculator

Paste a language sample and get type-token ratio (TTR), number of different words in the first 100 tokens (NDW-100, Miller 1981), and NDW per 50 utterances (NDW-50, SUGAR). Implements the standard SALT/SUGAR tokenisation rules and runs entirely in your browser.

Open tool

Language Sample Worksheet

Free printable and fillable language sample analysis worksheet for speech-language pathologists. Five columns (utterance #, transcription, morpheme count, grammatical Y/N, notes), configurable row count up to 100 utterances, browser print produces a clean PDF, and an inline running summary tracks total utterances, total morphemes, and rolling MLU as you fill it in.

Open tool

References

  1. ASHA (2024). Spoken Language Disorders. Practice Portal. American Speech-Language-Hearing Association.
  2. IDEA, 34 CFR §300.320(a)(2) — Measurable annual goals.
  3. Miller, J. F., & Iglesias, A. (2024). Systematic Analysis of Language Transcripts (SALT), current version.
  4. Pavelko, S. L., & Owens, R. E. (2017). Sampling Utterances and Grammatical Analysis Revised (SUGAR): New normative values for LSA measures. LSHSS, 48(3), 197-215.