Is Goblins free for teachers?

Yes. Goblins offers a generous free tier that includes 1 live feedback assignment per week, insights on student mastery and replays, and the ability to upload your own content. The Max plan adds unlimited assignments, superadaptive mode, grade exports, standards growth reports, and SSO/LMS integration, with per-student pricing for schools and districts.

How does Goblins work?

Three simple steps: (1) Share an assignment from thousands of standards-aligned topics using a share code. (2) Students speak and draw their thinking naturally, and Goblins responds with Socratic questioning that sounds just like their teacher. (3) Teachers get real-time formative insights showing what students struggle with and suggestions to inform instruction.

Does Goblins prevent cheating?

Yes. Goblins uses a purpose-trained AI model that evaluates cheating signals in real time. It can detect when students are copying answers, using external tools inappropriately, or not genuinely working through problems. Copy-pasting answers is also blocked.

Can I use my own voice with Goblins?

Yes. Teachers can record their voice and take a selfie to create a custom 3D avatar in about 30 seconds. Students then hear help in their own teacher's voice and see a familiar face, making the experience more personal and effective.

What math levels does Goblins cover?

Goblins covers math from Grade 5 through AP Calculus, including thousands of standards-aligned topics. Teachers can also upload their own content for custom assignments at any level.

Is Goblins FERPA compliant?

Yes. Goblins is FERPA and state compliant. Student data privacy and security are a top priority.

Does Goblins support English Language Learners?

Yes. Goblins supports 30+ languages so teachers don't have to translate every worksheet. It is especially helpful for ELL students who need accessible math instruction in their native language.

HS.SP

Conceptual Category Statistics and Probability

HS.SP.1Represent the distribution of data with plots on the real number line (stem plots, dot plots, histograms and box plots). (MP.4, MP.5)HS.SP.10Decide if a specified model is consistent with the results from a simulation. (MP.3, MP.6)HS.SP.11Recognize the purposes of and differences among sample surveys, experiments and observational studies; explain how randomization relates to each. (MP.3, MP.8)HS.SP.12Use data from a sample survey to estimate a population mean or proportion and explain how bias may be involved in the process. (MP.4, MP.7)HS.SP.13Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between estimates or statistics are significant. (MP.3, MP.8)HS.SP.14Describe events as subsets of a sample space. Use characteristics (or categories) of the outcomes, such as, (MP.1, MP.2)HS.SP.15Understand the concept of independence. (MP.1, MP.6)HS.SP.16Understand the concept of conditional probability. (MP.1, MP.3)HS.SP.17(+)Construct and interpret two-way frequency tables of data when two categories are associated with each object being classified. Use the two-way table as a sample space to decide whether events are independent and to approximate conditional probabilities. (MP.2, MP.4)HS.SP.18(+)Apply the General Multiplication Rule, P(A and B)=P(A)P(B|A)=P(B)P(A|B), in a uniform probability model and interpret the answer in terms of the model. (MP.1, MP.2)HS.SP.19Use permutations and combinations to compute probabilities. (MP.1, MP.8)HS.SP.2Use statistics appropriate to the shape of the numerical data distribution to compare center (median, mean) and spread (interquartile range when comparing medians and standard deviation when comparing means) of different data distributions. (MP.2, MP.6)HS.SP.20(+)Define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space; graph the corresponding probability distribution using the same appropriate graphical displays as for data distributions. (MP.3, MP.6)HS.SP.21(+)Calculate the expected value of a random variable; interpret it as the mean of the probability distribution and use the value in analyzing decisions. (MP.1, MP.8)HS.SP.22(+)Develop a probability distribution for a random variable. (MP.2, MP.8)HS.SP.23(+)Weigh the possible outcomes of a decision by assigning probabilities to payoff values and finding expected values. (MP.6, MP.8)HS.SP.3Interpret differences in shape, center and spread in the context of the distributions of the numerical data, accounting for the presence and possible effects of extreme data points (outliers). (MP.1, MP.7)HS.SP.4(+)When appropriate, fit a normal distribution to a numerical data set for given mean and standard deviation and then estimate population percentages using the Empirical Rule and recognize that there are data sets for which such a procedure is not appropriate. (MP.1, MP.3)HS.SP.5Summarize categorical data for two or more categories in frequency tables. Calculate and interpret joint, marginal and conditional relative frequencies (probabilities) in the context of the data, recognizing possible associations and trends in the data. (MP.2, MP.7)HS.SP.6Represent data on two quantitative variables on a scatter plot and describe how the explanatory and response variables are related. (MP.3, MP.4, MP.5)HS.SP.7Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data. (MP.1, MP.2)HS.SP.8Understand the role and purpose of correlation in linear regression. (MP.5, MP.6)HS.SP.9Understand statistics as a process for making inferences and justifying conclusions about population parameters based on a random sample from that population. (MP.1, MP.3)

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