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.

7.DSP

Data Analysis, Statistics, and Probability

7.DSP.1Investigate concepts of random sampling.7.DSP.1aUnderstand that a sample is a subset of a population and both possess the same characteristics.7.DSP.1bDifferentiate between random and non-random sampling.7.DSP.1cUnderstand that generalizations from a sample are valid only if the sample is representative of the population.7.DSP.1dUnderstand that random sampling is used to gather a representative sample and supports valid inferences about the population.7.DSP.2Draw inferences about a population by collecting multiple random samples of the same size to investigate variability in estimates of the characteristic of interest.7.DSP.3Visually compare the centers, spreads, and overlap of two displays of data (i.e., dot plots, histograms, box plots) that are graphed on the same scale and draw inferences about this data.7.DSP.4Compare the numerical measures of center (mean, median, mode) and variability (range, interquartile range, mean absolute deviation) from two random samples to draw inferences about the populations.7.DSP.5Investigate the concept of probability of chance events.7.DSP.5aDetermine probabilities of simple events.7.DSP.5bUnderstand that probability measures likelihood of a chance event occurring.7.DSP.5cUnderstand that the probability of a chance event is a number between 0 and 1.7.DSP.5dUnderstand that a probability closer to 1 indicates a likely chance event.7.DSP.5eUnderstand that a probability close to 1/2 indicates that a chance event is neither likely nor unlikely.7.DSP.5fUnderstand that a probability closer to 0 indicates an unlikely chance event.7.DSP.6Investigate the relationship between theoretical and experimental probabilities for simple events.7.DSP.6aDetermine approximate outcomes using theoretical probability.7.DSP.6bPerform experiments that model theoretical probability.7.DSP.6cCompare theoretical and experimental probabilities.7.DSP.7Apply the concepts of theoretical and experimental probabilities for simple events.7.DSP.7aDifferentiate between uniform and non-uniform probability models (distributions).7.DSP.7bDevelop both uniform and non-uniform probability models.7.DSP.7cPerform experiments to test the validity of probability models.7.DSP.8Extend the concepts of simple events to investigate compound events.7.DSP.8aUnderstand that the probability of a compound event is between 0 and 1.7.DSP.8bIdentify the outcomes in a sample space using organized lists, tables, and tree diagrams.7.DSP.8cDetermine probabilities of compound events using organized lists, tables, and tree diagrams.7.DSP.8dDesign and use simulations to collect data and determine probabilities.7.DSP.8eCompare theoretical and experimental probabilities for compound events.

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