Language focussed adaptive learning
Unlike earlier iterations of adaptive technology, where paths to fluency were highly dependent on specific benchmarks, Language Max uses an interleaved practice approach. It is not constricted by one simple step by step method where the learner can get stuck in one area (for example, grammar) before progressing to other areas. In this way, engagement is not hampered. While it may work as a rule that one master addition in math before moving on to multiplication in math the same cannot be said for language learning.
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Utilization of Experts towards true personalized learning
At Language Max we believe effective language learning requires a recognition of the complexities and nuances. For this reason, we employ language experts and engineers who are able to visualize an abstract path with a plethora of connections to other important elements of the discipline. This allows the learner to literally forge their own path, acquiring language at their own pace, but also with the combination of material suited to their personal learning style.
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Language Max has engaging cutting edge content, but the true genius of the system is the ability to resequence the delivery of the content so that they are always presented with the next step best suited to the learner as they work towards a solution. This is made possible by the incredible team we have assembled from multiple backgrounds. Our adaptive learning engine built by this team stands apart in its ability to detect subtle data clues as to what areas the learner struggles with and what understanding they may be lacking in order to continue moving forward.
Finally, our company understands the true goal of language learning is improved communication, which necessitates effective input and output. For this reason, we have utilized Natural Language Processing (NLP) and semantic coding technologies to build better chatbots, that are capable of decoding intended meanings that may or may not have human errors.
Finally, our company understands the true goal of language learning is improved communication, which necessitates effective input and output. For this reason, we have utilized Natural Language Processing (NLP) and semantic coding technologies to build better chatbots, that are capable of decoding intended meanings that may or may not have human errors.